Distance Measurements Research Articles

Comfort distance between patients and pharmacists during medication instruction: A prospective observational study at a cancer chemotherapy center.

The distance from the patient is a crucial factor in the communication with patients. The distance between patients and pharmacists varies depending on several factors. In this study, we aimed to investigate the relationship between comfort distance and patients' physical condition and mood by measuring this distance at a chemotherapy center. A total of 114 patients were surveyed regarding their physical condition and mood. The distance at which the patients were best able to talk to the pharmacists was measured. For comfort distance measurement, the pharmacists were instructed to approach or move away from the patients. The correlation between physical condition, mood, and comfort distance was examined in both male and female patients, and no significant correlation was found; however, there was a strong correlation between physical condition and mood in female patients. We looked at correlations by further dividing patients into those over and under 65 years of age and found a slight correlation with comfort distance in women under 65. They tended to shorten the distance when they felt well and lengthen the distance when they felt not well. No correlation was found between physical condition or mood and comfort distance in male or female. A slight correlation was observed when age was included; however, the results were not satisfactory. By directly measuring the distance in actual patients, we obtained an actual measurement of the comfort distance that synthesized the patient's condition and various backgrounds during chemotherapy, providing a foothold for future studies.

Microwave photonics frequency scanning approach to absolute distance measurement

An absolute distance measurement approach based on microwave photonics is proposed to provide a high-resolution and non-ambiguity range measurement solution by microwave frequency scanning. The microwave interference spectrum containing optical path difference information can be generated through two rounds of intensity modulation. The approach combines the characteristics of easy focusing and collimation of light waves, as well as easy manipulation of microwaves, to achieve accurate absolute distance measurement. The experimental results show that high-precision measurements can be achieved within the frequency scanning range of only 2 GHz. The error of the distance measurement is less than ±0.079 mm. In addition, the effectiveness of the scanning pattern and the data processing algorithm are verified.

An obstacle avoidance safety detection algorithm for power lines combining binocular vision technology and improved object detection

In this paper, a framework of obstacle avoidance algorithm applied to power line damage safety distance detection is constructed, and its overall architecture and key processes are described in detail. The system design covers three core modules: visual data acquisition and preliminary processing, accurate target recognition and distance measurement, and system error analysis and correction. In the visual data processing chain, we deeply analyze every step from image acquisition to preprocessing to feature extraction, aiming to enhance the adaptability of applications to complex scenes. The target recognition and distance estimation part integrates advanced technology of deep learning to improve the reliability of recognition accuracy and distance estimation. In addition, many common error sources, such as system bias, parallax discontinuity, fluctuation of illumination conditions, etc., are discussed in depth, and corresponding correction strategies are proposed to ensure the accuracy and stability of the system, which provides powerful technical support for achieving efficient and accurate safety monitoring. Specifically, by carefully adjusting the learning rate, convolution kernel size, batch size, pooling layer type, and number of hidden layer nodes, we succeeded in improving the overall accuracy from the initial average of 92.4–95%, and the error rate decreased accordingly.

Video text tracking with transformer-based local search

Video text tracking is a highly significant branch in the field of multi-object tracking (MOT), aiming to detect all text instances in video frames and construct trajectories for each text. Conventional text tracking methods typically follow the Tracking-By-Detection (TbD) paradigm which involves two separate steps of detection and association. In complex scenarios, abundant detection omission of text instances is caused by exposure, occlusion, motion blur, and the like, leading to text trajectory breakpoints in the TbD methods. To this end, video text tracking with transformer-based local search (LSTrack) is proposed in this paper. We utilize historical trajectory information to estimate the approximate search areas where omitted texts are located. In this case, our local search tracker leverages the text images in historical trajectories as references to directly recall text instances in the search areas. In addition, our tracking framework can combine the explicit semantic information output from an OCR model to obtain the semantic version (SV) of LSTrack, which for the first time uses the text edit distance as the distance measurement in matching stages to achieve better text tracking results. Ultimately, our method achieves an advanced performance on several public benchmarks. In particular, LSTrack (SV) achieves state-of-the-art performance on the Minetto benchmark.

果园环境中巡检机器人的 SLAM 和路径规划方法研究

Orchard robots play a crucial role in agricultural production. Autonomous navigation serves as the foundation for orchard robots and eco-unmanned farms. Accurate sensing and localization are prerequisites for achieving autonomous navigation. However, current vision-based navigation solutions are sensitive to environmental factors, such as light, weather, and background, which can affect positioning accuracy. Therefore, they are unsuitable for outdoor navigation applications. LIDAR provides accurate distance measurements and is suitable for a wide range of environments. Its immunity to interference is not affected by light, colour, weather, or other factors, making it suitable for low objects and complex orchard scenes. Therefore, LiDAR navigation is more suitable for orchard environments. In complex orchard environments, tree branches and foliage can cause Global Positioning System (GNSS) accuracy to degrade, resulting in signal loss. Therefore, the major challenge that needs to be addressed is generating navigation paths and locating the position of orchard robots. In this paper, an improved method for Simultaneous Localization and Mapping (SLAM) and A-star algorithm is proposed. The SLAM and path planning method designed in this study effectively solves the problems of insufficient smoothness and large curvature fluctuation of the path planned in the complex orchard environment, and improves the detection efficiency of the robot. The experimental results indicate that the method can consistently and accurately fulfil the robot's detection needs in intricate orchard environments.

Localization and circumnavigation of an unknown target using distance measurements in 3D space

This paper investigates the problem of localization and circumnavigation of an unknown stationary target in 3D space using distance measurements. An estimator is first designed in 2D space to locate the target, with which a control protocol is proposed to force the agent to travel along a circular trajectory around the target. Then the algorithm is extended to 3D space to circumnavigate the target under the condition of obtaining gravity direction additionally. In particular, the agent moves alternately in the X-Y horizontal planes and the X-Z vertical planes in different patterns under different switching conditions, which is beneficial to monitor the target from a broader perspective. Meanwhile, the rigorous stability analyses of two cases are given in terms of the Lyapunov method. Finally, the effectiveness of the proposed algorithms is verified through numerical simulations and a real experiment.

Defect Rate Prediction in Manufacturing Process Using K-Nearest Neighbor Algorithm

The efficiency and effectiveness in the manufacturing industry are significantly impacted by artificial intelligence technology. An important application involves the improvement of product quality, which is measurable through the defects occurring during the production process. This research is aimed at predicting defects in the manufacturing process using the K-Nearest Neighbor (KNN) algorithm with various distance measurement methods, namely Euclidean, Minkowski, and Manhattan distances. The research methodology is composed of four stages: dataset collection, data preprocessing, modeling, and evaluation. The focus of this research is on the optimal K value and the conditions that yield the highest accuracy, considering various scenarios of training and test data splitting ratios and different random state values. The test results indicate that the Minkowski distance method, with a data division ratio of 80% for training data, 20% for test data, and a random state value of 32, provides the best performance, with an optimal K value of 10 and an accuracy of 86.41%.

Bio-Inspired Strategies Are Adaptable to Sensors Manufactured on the Moon.

Bio-inspired strategies for robotic sensing are essential for in situ manufactured sensors on the Moon. Sensors are one crucial component of robots that should be manufactured from lunar resources to industrialize the Moon at low cost. We are concerned with two classes of sensor: (a) position sensors and derivatives thereof are the most elementary of measurements; and (b) light sensing arrays provide for distance measurement within the visible waveband. Terrestrial approaches to sensor design cannot be accommodated within the severe limitations imposed by the material resources and expected manufacturing competences on the Moon. Displacement and strain sensors may be constructed as potentiometers with aluminium extracted from anorthite. Anorthite is also a source of silica from which quartz may be manufactured. Thus, piezoelectric sensors may be constructed. Silicone plastic (siloxane) is an elastomer that may be derived from lunar volatiles. This offers the prospect for tactile sensing arrays. All components of photomultiplier tubes may be constructed from lunar resources. However, the spatial resolution of photomultiplier tubes is limited so only modest array sizes can be constructed. This requires us to exploit biomimetic strategies: (i) optical flow provides the visual navigation competences of insects implemented through modest circuitry, and (ii) foveated vision trades the visual resolution deficiencies with higher resolution of pan-tilt motors enabled by micro-stepping. Thus, basic sensors may be manufactured from lunar resources. They are elementary components of robotic machines that are crucial for constructing a sustainable lunar infrastructure. Constraints imposed by the Moon may be compensated for using biomimetic strategies which are adaptable to non-Earth environments.

Major technical barriers and a high-precision distance measurement method for frequency modulated continuous wave LiDAR

Frequency modulated continuous wave (FMCW) light detection and ranging (LiDAR) is considered as one of the most promising 3D imaging technologies by virtue of its single-photon detection sensitivity, high interference immunity, and high precision. In this paper, major technical barriers that limit the precision of FMCW LiDAR range are summarized and analyzed, and solutions to improve the measurement precision are presented. In addition, a novel method for high precision ranging of FMCW LiDAR is proposed, which can be applied to measure target distance in both active and passive environments. The proposed method has a precision of 11 μm in passive environments.

Planck Length Emerging as the Invariant Quantum Minimum Effective Length Determined by the Heisenberg Uncertainty Principle in Manifestly Covariant Quantum Gravity Theory

The meaning of the quantum minimum effective length that should distinguish the quantum nature of a gravitational field is investigated in the context of manifestly covariant quantum gravity theory (CQG-theory). In such a framework, the possible occurrence of a non-vanishing minimum length requires one to identify it necessarily with a 4-scalar proper length s.It is shown that the latter must be treated in a statistical way and associated with a lower bound in the error measurement of distance, namely to be identified with a standard deviation. In this reference, the existence of a minimum length is proven based on a canonical form of Heisenberg inequality that is peculiar to CQG-theory in predicting massive quantum gravitons with finite path-length trajectories. As a notable outcome, it is found that, apart from a numerical factor of O1, the invariant minimum length is realized by the Planck length, which, therefore, arises as a constitutive element of quantum gravity phenomenology. This theoretical result permits one to establish the intrinsic minimum-length character of CQG-theory, which emerges consistently with manifest covariance as one of its foundational properties and is rooted both on the mathematical structure of canonical Hamiltonian quantization, as well as on the logic underlying the Heisenberg uncertainty principle.

Most frequent value analysis of distance measurements to M87

Abstract We reanalyze the recent compilation of distance measurements to M87 by collecting the data from published literature. Different from the traditional statistical methods, based on the principle of minimum information loss, we use a robust most frequent value (MFV) procedure to estimate the distance to M87, irrespective of the Gaussian or non-Gaussian distributions. The MFV-based robust estimate for the M87 distance modulus is given by $31.09^{+0.04}_{-0.03}$(statistical)$^{+0.05}_{-0.07}$ (systematic) mag corresponding to a 68.27% confidence interval, whereas the result of combining the two uncertainties in quadrature is $31.09^{+0.06}_{-0.08}$ mag. We also construct the error distributions of M87 distance moduli values according to the weighted mean, median, and MFV, which is non-Gaussian. This demonstrates that the MFV method offers a more accurate and robust estimate of the distance to M87 compared to methods that depend on the unfulfilled assumption of Gaussianity.

Inter-satellite tracking methods and applications: A comprehensive survey

The accurate measurement of inter-satellite distances is fundamental to the successful operation of distributed spacecraft missions, facilitating diverse applications ranging from scientific exploration to navigation and communication. This study comprehensively overviews applications requiring inter-satellite tracking by analyzing 44 multi-spacecraft missions. These missions are divided into seven categories based on their use of inter-satellite distance measurements. Each category necessitates varying levels of accuracy, prompting the utilization of distinct tracking methods. The analysis reveals that missions near Earth typically rely on Global Navigation Satellite Systems measurements, achieving millimeter-level accuracy, while lunar missions opt for radio ranging for centimeter-level accuracy. Inter-satellite Laser Ranging Interferometry emerges as the preferred method for missions demanding exceptionally high accuracy (nanometer to picometer range), such as those dedicated to gravitational wave detection and gravimetry. Notably, the analysis identifies a burgeoning trend towards the adoption of Inter-satellite Laser Transponder Ranging, capable of achieving sub-millimeter accuracy. Furthermore, this work proposes a novel concept: integrating inter-satellite ranging with Laser Communication Terminal (LCTs), either to substitute for existing tracking methods or to enhance measurement accuracy within established frameworks. However, its full potential rests upon the successful adaptation of existing LCTs for ranging functionality without compromising data rates. Future research will play a critical role in quantifying achievable ranging performance by characterizing systematic errors within LCTs.

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.

A Review of Perception Technologies for Berry Fruit-Picking Robots: Advantages, Disadvantages, Challenges, and Prospects

Berries are nutritious and valuable, but their thin skin, soft flesh, and fragility make harvesting and picking challenging. Manual and traditional mechanical harvesting methods are commonly used, but they are costly in labor and can damage the fruit. To overcome these challenges, it may be worth exploring alternative harvesting methods. Using berry fruit-picking robots with perception technology is a viable option to improve the efficiency of berry harvesting. This review presents an overview of the mechanisms of berry fruit-picking robots, encompassing their underlying principles, the mechanics of picking and grasping, and an examination of their structural design. The importance of perception technology during the picking process is highlighted. Then, several perception techniques commonly used by berry fruit-picking robots are described, including visual perception, tactile perception, distance measurement, and switching sensors. The methods of these four perceptual techniques used by berry-picking robots are described, and their advantages and disadvantages are analyzed. In addition, the technical characteristics of perception technologies in practical applications are analyzed and summarized, and several advanced applications of berry fruit-picking robots are presented. Finally, the challenges that perception technologies need to overcome and the prospects for overcoming these challenges are discussed.

Fuzzy Logic Algorithm Optimization for Safe Distance Control on Arduino-Based Reverse Parking System and SRF04 Sensor

This research aims to develop a smart parking system that can accurately control the distance between vehicles and obstacles during reverse parking maneuvers. By integrating fuzzy logic algorithms into the system, this study seeks to improve the precision and reliability of distance control, thereby improving the overall safety of parking operations. The utilization of the Arduino platform and the SRF04 sensor allows real-time data processing and accurate distance measurement, i.e. this research contributes to the effectiveness of the proposed system. The application of fuzzy logic optimization in this context is expected to provide a powerful solution for safe reverse parking, offering potential benefits in terms of comfort and accident prevention in parking scenarios, especially for cars that still do not have obstacle detection sensors at the rear of the car

Heading estimation algorithm for pedestrian navigation using a dual-foot-mounted IMU with a millimetre wave radar

Heading estimation is crucial in pedestrian navigation systems based on IMUs, but accuracy often suffers degradation due to error accumulation. This study introduces a novel heading estimation algorithm using dual-foot-mounted IMUs with a millimeter-wave radar to enhance accuracy. The algorithm includes a Single-foot Multiconditional Constraint Algorithm (SMC-A), adaptive step length constraint, and bipedal maximum heading difference constraint. Firstly, the SMC-A utilizes zero speed correction, zero angular rate updates, and self-observing heading error corrections through extended Kalman filtering. Then, the adaptive step length constraint limits pedestrian motion, and the bipedal constraint corrects heading errors. Finally, the Bipedal Multiconditional Constraint Algorithm (BMC-A) incorporates millimeter-wave radar measurements of foot distance. Experiments show heading deviations of 3.74° and 4.03° are obtained for the left and right feet over a 30-minute, 1.45 km trial. The new algorithm improves heading accuracy by 68.19 % and 70.67 % for the left and right feet, respectively, compared to traditional SMC-A.

Isokinetic Knee Strengthening Impact on Physical and Functional Performance, Pain Tolerance, and Quality of Life in Overweight/Obese Women with Patellofemoral Pain Syndrome.

Background/Objectives: Patellofemoral syndrome is a common osteoarticular condition that affects many individuals. Various treatment options are available, with a significant emphasis on targeted muscle-strengthening exercises. The purpose of this study was to investigate the effect of isokinetic muscle strengthening on muscle strength, joint range of motion, quality of life, physical performance, and pain tolerance in overweight/obese women with patellofemoral syndrome. Methods: Twenty-four overweight or obese women with patellofemoral syndrome participated in the study during September and October 2023 in a private medical facility for physical medicine and functional rehabilitation. They were randomly assigned to one of two groups for six weeks of isokinetic muscle strengthening. The first group (ISO.G) followed a rehabilitation program combined with isokinetic muscle strengthening. A second group (PCM.G) followed a rehabilitation program that includes an isokinetic protocol in passive compensation movement. The extensors' peak torque was measured before and after training. Results: The flexors' peak torque, stair climbing test, 10 m walk, chair lift, monopodal support, goniometric knee flexion test, heel-buttock distance measurement, pain, and quality of life scores improved significantly in both groups. The ISO.G, on the other hand, benefited from a significant increase in quadriceps muscle strength revealed by the extensors' peak torque. Conclusions: For the treatment of patellofemoral syndrome, isokinetic muscle strengthening in concentric mode appears to have a significant advantage over the classic rehabilitation program with isokinetic passive compensation, particularly in muscle strength gain, in addition to the improvement of joint range of motion, quality of life, physical performance, and pain tolerance. Isokinetic training may be recommended as a beneficial approach for the rehabilitative treatment of patellofemoral pain syndrome in overweight/obese women.

Gap distance sensing for non-magnetic medium based on magnetoelectric effect under spatial separation condition

This paper presents a novel non-contact spatial gap distance sensing (GDS) method that can provide distance information in spatial separation conditions. In many applications, such as enclosed environments, it could not provide the desired measurement of gap distance of internal non-magnetic medium due to the constraints of physical barriers and poor accessibility. Therefore, a non-invasive sensing system is designed to measure spatial gap distance for non-magnetic medium. The developed sensor system consists of a pair of heteropolar permanent magnets (PMs), a non-magnetic medium, a magnetostrictive-piezoelectric composite unit and an external space, which has the function of spatial separation measurement. By exploiting the magnetoelectric effect, the magneto-machine-electric conversion is achieved by sensing the spatial magnetic field generated by the heteropolar PMs. The coupling modeling, analysis and calibration of sensing system are conducted, and the system prototype is designed and manufactured. Additionally, the performances of the GDS are experimentally validated. Static gap distance (plate thickness) measurements of the plate and variable gap distance (instant water height) measurements of water are performed, and resolution, vibration, and drift tests are carried out. The results show the accuracy and stability of non-contact spatial gap distance detection for non-magnetic medium, highlighting its potential in various applications.

Proof of principle X-ray reflection mass measurement of the black hole in H1743−322

ABSTRACT The black hole X-ray binary H1743−322 lies in a region of the Galaxy with high extinction, and therefore it has not been possible to make a dynamical mass measurement. In this paper, we make use of a recent model which uses the X-ray reflection spectrum to constrain the ratio of the black hole mass to the source distance. By folding in a reported distance measurement, we are able to estimate the mass of the black hole to be $12\pm 2~\text{M}_\odot {}$ ($1\sigma$ credible interval). We are then able to revise a previous disc continuum fitting estimate of black hole spin $a_*$ (previously relying on a population mass distribution) using our new mass constraint, finding $a_*=0.47\pm 0.10$. This work is a proof of principle demonstration of the method, showing it can be used to find the mass of black holes in X-ray binaries.

Puzzle: taking livestock tracking to the next level

Animal behavior is a critical aspect for a better understanding and management of animal health and welfare. The combination of cameras with artificial intelligence holds significant potential, particularly as it eliminates the need to handle animals and allows for the simultaneous measurement of various traits, including activity, space utilization, and inter-individual distance. The primary challenge in using these techniques is dealing with the individualization of data, known as the multiple object tracking problem in computer science. In this article, we propose an original solution called “Puzzle.” Similar to solving a puzzle, where you start with the border pieces that are easy to position, our approach involves commencing with video sequences where tracking is straightforward. This initial phase aims to train a Convolutional Neural Network (CNN) capable of deriving the appearance clues of each animal. The CNN is then used on the entire video, together with distance-based metrics, in order to associate detections and animal id. We illustrated our method in the context of outdoor goat tracking, achieving a high percentage of good tracking, exceeding 90%. We discussed the impact of different criteria used for animal ID association, considering whether they are based solely on location, appearance, or a combination of both. Our findings indicate that, by adopting the puzzle paradigm and tailoring the appearance CNN to the specific video, relying solely on appearance can yield satisfactory results. Finally, we explored the influence of tracking efficacy on two behavioral studies, estimating space utilization and activity. The results demonstrated that the estimation error remained below 10%. The code is entirely open-source and extensively documented. Additionally, it is linked to a data-paper to facilitate the training of any automatic detection algorithm for goats, with the goal of fostering open access within the deep-learning livestock community.