Velocity Of Object Research Articles

From Innocuous Contact to Sudden Death in Sports: An Overview of Commotio Cordis

Commotio Cordis (CC) is a rare but potentially fatal condition that occurs when a blunt impact to the chest causes ventricular fibrillation (VF) and sudden cardiac death. CC is most common in teenage males who participate in sports, but it can occur in people of all ages. The mechanical distortion of the myocardium caused by trauma can lead to inappropriate depolarization and myocardial firing, which can cause arrhythmias. Immediate identification and defibrillation are crucial to reducing mortality rates. Currently available screening tools for CC, such as the electrocardiogram (EKG), stress EKG, or echocardiogram, lack sensitivity for primary prevention. The pathogenesis of CC is influenced by a variety of factors, including the location, velocity, shape, and hardness of the impact object, as well as biological characteristics like gender, chest wall flexibility, and genetic susceptibility. The ion channels involved in arrhythmia generation in CC are thought to be stretch-activated channels (SACs). Diagnosis is based on a history of chest wall blunt trauma, and treatment includes immediate supportive care and defibrillation according to BLS and ACLS protocols. The currently available chest wall protectors have not been shown to prevent CC, and improving sports equipment may help reduce the incidence of CC. A famous American football player incident in which he collapsed after making a tackle during the first quarter of the Bills' game against the Cincinnati Bengals on January 2, 2023, is a notable case of commotio cordis (CC) in a football player. This incident is unusual because helmets are not typically associated with CC, which is usually caused by external blunt force trauma to the chest with small balls, hockey pucks, or even a blow. The player, who fortunately survived the event, was relatively older than the recorded mean age for CC and has a more rigid chest wall. It is unclear whether this case is an exception or if helmets can pose a risk for CC in certain circumstances. The case highlights the need for continued research and awareness of CC to improve prevention and management strategies for sudden cardiac arrest in sports. Despite being an ancient condition, CC remains a tragic event, and further research is needed to improve preventive measures and reduce mortality rates.

Analysis of the Variety of the Relativistic Approach Velocity of Objects Based on the Data of the Large Hadron Collider

Analysis of the Variety of the Relativistic Approach Velocity of Objects Based on the Data of the Large Hadron Collider

Predicting object properties based on movement kinematics

In order to grasp and transport an object, grip and load forces must be scaled according to the object’s properties (such as weight). To select the appropriate grip and load forces, the object weight is estimated based on experience or, in the case of robots, usually by use of image recognition. We propose a new approach that makes a robot’s weight estimation less dependent on prior learning and, thereby, allows it to successfully grasp a wider variety of objects. This study evaluates whether it is feasible to predict an object’s weight class in a replacement task based on the time series of upper body angles of the active arm or on object velocity profiles. Furthermore, we wanted to investigate how prediction accuracy is affected by (i) the length of the time series and (ii) different cross-validation (CV) procedures. To this end, we recorded and analyzed the movement kinematics of 12 participants during a replacement task. The participants’ kinematics were recorded by an optical motion tracking system while transporting an object, 80 times in total from varying starting positions to a predefined end position on a table. The object’s weight was modified (made lighter and heavier) without changing the object’s visual appearance. Throughout the experiment, the object’s weight (light/heavy) was randomly changed without the participant’s knowledge. To predict the object’s weight class, we used a discrete cosine transform to smooth and compress the time series and a support vector machine for supervised learning from the achieved discrete cosine transform parameters. Results showed good prediction accuracy (up to 95%, depending on the CV procedure and the length of the time series). Even at the beginning of a movement (after only 300 ms), we were able to predict the object weight reliably (within a classification rate of 88-94%).

Force on a moving object in an ideal quantum gas

We consider a heavy external object moving in an ideal gas of light particles. Collisions with the gas particles transfer momentum to the object, leading to a force that is proportional to the object's velocity but in the opposite direction. In an ideal classical gas at temperature T, the force acting on the object is proportional to T. Quantum statistics causes a deviation from the T-dependence and shows that the force scales with T2 at low temperatures. At T = 0, the force vanishes in a Bose gas but is finite in a Fermi gas.

Target motion misjudgments reflect a misperception of the background; revealed using continuous psychophysics.

Determining the velocities of target objects as we navigate complex environments is made more difficult by the fact that our own motion adds systematic motion signals to the visual scene. The flow-parsing hypothesis asserts that the background motion is subtracted from visual scenes in such cases as a way for the visual system to determine target motions relative to the scene. Here, we address the question of why backgrounds are only partially subtracted in lab settings. At the same time, we probe a much-neglected aspect of scene perception in flow-parsing studies, that is, the perception of the background itself. Here, we present results from three experienced psychophysical participants and one inexperienced participant who took part in three continuous psychophysics experiments. We show that, when the background optic flow pattern is composed of local elements whose motions are congruent with the global optic flow pattern, the incompleteness of the background subtraction can be entirely accounted for by a misperception of the background. When the local velocities comprising the background are randomly dispersed around the average global velocity, an additional factor is needed to explain the subtraction incompleteness. We show that a model where background perception is a result of the brain attempting to infer scene motion due to self-motion can account for these results.

Implementation of ranging system using ultrasonic sensor and microcontroller unit for aviation industries

In the aviation industry, radio detection and ranging is employed to evaluate the height, distance, bearing, size, trajectory or velocity of both dynamic and static objects. Ultrasonic waves can also be used instead of electromagnetic waves in ultrasonic sensor. This research paper presents the real time implementation of a less-expensive ultrasonic detection and ranging system built based on Arduino Uno 328 microcontroller. It utilizes ultrasonic sensors attached to a servo motor which rotates from 00 to 3600 to detect an object in range within 400cm which passes the data to an Arduino microcontroller, this then gives visual representation on the software called Processing-3.5.4 software (Processing development Environment) java language. The Processing displays on an LED (Light Emitting Diode) screen graphical information such as bearing, size, range and position of the object depends on the proximity of the object to the ranging system. Result obtained shows a maximum a good maximum error of 7 % for the test object considered. Hence, this system can be deployed in the field.

A Learning-Based Object Tracking Strategy Using Visual Sensors and Intelligent Robot Arm

This paper focuses on addressing the visual tracking problem using learning-based methods for object tracking tasks. This problem contains a major difficulty, i.e., how to acquire a satisfactory generalization ability of the developed system? In this paper, firstly, the object state tracking system, including a camera-in-hand, a 3D camera and a Rethink Baxter robot, is introduced. The problem formulation is also presented. Secondly, we propose a Kalman-based estimation strategy to acquire the object’s state. In addition, a learning-based tracking controller is developed using the Gaussian mixture models (GMM) method to steer the robot end-effector to track the mobile object. Thirdly, to guarantee system stability (i.e., the position and velocity errors between the object and end-effector will always converge to zeros), the controller parameter constraints are derived, which is a theoretical contribution of this paper. The controller parameter adjustment is avoided by the proposed training process. Thus, the proposed method becomes easy to implement, which is a practical contribution. Finally, the effectiveness of the proposed method is demonstrated by simulation and experimental examples, and the proposed method has satisfactory generalization ability. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This paper studies object tracking problems for different practical applications, such as industrial cutting, grasping and dynamic monitoring. Different trajectory tracking methods have been widely applied in the industrial area. However, users always complain that when the object or trajectory is changed, the tracking controller more or less needs to be re-adjusted. This re-adjust process always requires professional knowledge and programming experience, and thus a factory must employ some professional engineers. In addition, since the objects may be diverse in shape, color and size, to acquire the accurate object position and velocity, an appropriate solution is necessary. Motivated by the above introductions, this paper aims to develop a learning-based controller to track different complex trajectories without frequent and specific parameter adjustment processes. Firstly, a visual measurement system is developed to quickly find and estimate the position and velocity of an object. Secondly, with the object’s information, the learning from demonstration method (GMM method) is applied for the control policy design. Thirdly, the detailed system stability analysis is presented, and the corresponding controller parameter constraints are derived and considered in the proposed control policy. Subsequently, with the demonstration data, the packaged learning algorithm will automatically compute the controller parameters, and users can change the controller performance only by providing the desired demonstrations. In summary, this paper proposes a systematic object tracking solution, and it may bring a new idea to develop a practical object tracking system, using both the learning-based methods to improve the ability of generalization for tracking different objects.

Fatigue evaluation on the effect of impact pits with various impact conditions by a CDM‐based approach

AbstractThis work aims to quantitatively evaluate the influence of the impact velocity, location, and angle of a spherical foreign object on fatigue lives. A continuum damage mechanics (CDM)–based fatigue approach is proposed to evaluate the fatigue behavior. The experiments on a cast AlSi7Mg1 aluminum alloy with a low‐velocity impact pit are used to validate the proposed fatigue model. The comparison between the predicted lives with the experimental data indicates an excellent agreement. Based on the proposed model, the effects of various impact velocities, locations, and angles are investigated. It is found that the velocities have negligible influence on the fatigue life in low‐velocity impact cases once the pit depth is the same. The most dangerous impact location for 90° normal impact cases is inferred to be a distance away from the boundary surface. The 45° transverse impact close to the boundary is different from other studied oblique impacts and has a longer life than the corresponding normal impact. This study uses a feasible method to predict the effects of different impact conditions and provides useful guidance for engineering applications.

Long-Range Navigation in Complex and Dynamic Environments with Full-Stack S-DOVS

Robotic autonomous navigation in dynamic environments is a complex problem, as traditional planners may fail to take dynamic obstacles and their variables into account. The Strategy-based Dynamic Object Velocity Space (S-DOVS) planner has been proposed as a solution to navigate in such scenarios. However, it has a number of limitations, such as inability to reach a goal in a large known map, avoid convex objects, or handle trap situations. In this article, we present a modified version of the S-DOVS planner that is integrated into a full navigation stack, which includes a localization system, obstacle tracker, and novel waypoint generator. The complete system takes into account robot kinodynamic constraints and is capable of navigating through large scenarios with known map information in the presence of dynamic obstacles. Extensive simulation and ground robot experiments demonstrate the effectiveness of our system even in environments with dynamic obstacles and replanning requirements, and show that our waypoint generator outperforms other approaches in terms of success rate and time to reach the goal when combined with the S-DOVS planner. Overall, our work represents a step forward in the development of robust and reliable autonomous navigation systems for real-world scenarios.

An Extended Membrane System with Monodirectional Tissue-like P Systems and Enhanced Particle Swarm Optimization for Data Clustering

In order to establish a highly efficient P system for resolving clustering problems and overcome the computation incompleteness and implementation difficulty of P systems, an attractive clustering membrane system, integrated with enhanced particle swarm optimization (PSO) based on environmental factors and crossover operators and a distributed parallel computing model of monodirectional tissue-like P systems (MTP), is constructed and proposed, which is simply named ECPSO-MTP. In the proposed ECPSO-MTP, two kinds of evolution rules for objects are defined and introduced to rewrite and modify the velocity of objects in different elementary membranes. The velocity updating model uses environmental factors based on partitioning information and randomly replaces global best to improve the clustering performance of ECPSO-MTP. The crossover operator for the position of objects is based on given objects and other objects with crossover probability and is accomplished through the hybridization of the global best of elementary membranes to reject randomness. The membrane structure of ECPSO-MTP is abstracted as a network structure, and the information exchange and resource sharing between different elementary membranes are accomplished by evolutional symport rules with promoters for objects of MTP, including forward and backward communication rules. The evolution and communication mechanisms in ECPSO-MTP are executed repeatedly through iteration. At last, comparison experiments, which are conducted on eight benchmark clustering datasets from artificial datasets and the UCI Machine Learning Repository and eight image segmentation datasets from BSDS500, demonstrate the effectiveness of the proposed ECPSO-MTP.

Research on Enhanced Orbit Prediction Techniques Utilizing Multiple Sets of Two-Line Element

Acquiring accurate space object orbits is crucial for many applications such as satellite tracking, space debris detection, and collision avoidance. The widely used two-line element (TLE) method estimates the position and velocity of objects in space, but its accuracy can be limited by various factors. A combination of multiple TLEs and advanced modeling techniques such as batch least squares differential correction and high-precision numerical propagators can significantly improve TLE accuracy and reliability, ensuring better space object surveillance. Previous studies analyzed additional factors that may influence TLE accuracy and evaluated the accuracy of Starlink TLE using precise ephemeris data from SpaceX. The results indicate that utilizing multiple TLEs for precise orbit determination can significantly enhance the performance of orbit prediction methods, particularly when compared to SGP4. By leveraging 10-day Starlink TLEs, the accuracy of 5-day predictions can be improved by approximately twofold. Additionally, producing two pseudo-observations within an orbital period near the TLE epoch yields the greatest effect on prediction accuracy, with this distribution of pseudo-observations increasing accuracy by approximately 10% compared to a uniform distribution. Further research can explore more data fusion and machine learning approaches to optimize operations in space.

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.

4D iRIOM: 4D Imaging Radar Inertial Odometry and Mapping

Millimeter wave radar can measure distances, directions, and Doppler velocity for objects in harsh conditions such as fog. The 4D imaging radar with both vertical and horizontal data resembling an image can also measure objects' height. Previous studies have used 3D radars for ego-motion estimation. But few methods leveraged the rich data of imaging radars, and they usually omitted the mapping aspect, thus leading to inferior odometry accuracy. This paper presents a real-time imaging radar inertial odometry and mapping method, iRIOM, based on the submap concept. To deal with moving objects and multipath reflections, we use the graduated non-convexity method to robustly and efficiently estimate ego-velocity from a single scan. To measure the agreement between sparse non-repetitive radar scan points and submap points, the distribution-to-multi-distribution distance for matches is adopted. The ego-velocity, scan-to-submap matches are fused with the 6D inertial data by an iterative extended Kalman filter to get the platform's 3D position and orientation. A loop closure module is also developed to curb the odometry module's drift. To our knowledge, iRIOM based on the two modules is the first 4D radar inertial SLAM system. On our and third-party data, we show iRIOM's favorable odometry accuracy and mapping consistency against the FastLIO-SLAM and the EKFRIO. Also, the ablation study reveal the benefit of inertial data versus the constant velocity model, and scan-to-submap matching versus scan-to-scan matching.

A fast tracking method for magnetic abnormalities using distributed Overhauser magnetometer system based on genetic algorithm.

Magnetic anomaly detection technologies have been widely used for tracking moving targets. In this paper, we present a fast-tracking method for magnetic abnormalities using a distributed Overhauser magnetometer system based on the genetic algorithm. Our proposed framework of the Overhauser magnetometer system employs multiple sensors to eliminate background interference, and the genetic algorithm efficiently solves magnetic anomaly data without requiring the derivation of the objective function. Test platforms were built to evaluate the distributed Overhauser magnetometer system and the genetic algorithm. Results from the natural outdoor magnetism laboratories showed that the noise of our presented magnetometers was below 0.134 nT. The optimal factors for solution precision and effectiveness in the genetic algorithm were obtained from the simulation. Moreover, the outdoor tracking experiments indicated that the proposed method could accurately and quickly detect the moving ferromagnetic object within 6.9% maximum positioning error in 0.55m, and the tracking precision of the object velocity can get 5.88% maximum error in 4.33 km/h.

Marine Object Segmentation and Tracking by Learning Marine Radar Images for Autonomous Surface Vehicles

This paper proposes a radar image segmentation and tracking method by learning radar images for autonomous surface vehicles. To identify marine objects from radar images, we propose a deep neural network named the dual path squeeze and excitation network (DPSE-Net). By learning the radar images, the proposed DPSE-Net is designed to segment every pixel of the radar images into four classes: marine objects, land, noise, and background. The proposed DPSE-Net shows the best performance in radar image segmentation while operating in real-time, compared to state-of-the-art real-time image segmentation network models. In addition, we design a real-time moving object tracking algorithm for estimating the position and velocity of marine objects based on deep simple online real-time tracking with a deep association metric (DeepSORT), a widely used tracking algorithm. The existing DeepSORT algorithm uses the intersection over union (IoU) metric and a deep appearance descriptor for data association, but since they are not suitable for radar images, successive tracking is difficult. To solve this problem, a new data association metric suitable for radar images is proposed. The field tests in ocean environments confirm that the proposed method performs better in marine object segmentation and tracking.

Elliptic Localization of a Moving Object by Transmitter at Unknown Position and Velocity: A Semidefinite Relaxation Approach

This paper investigates the elliptic localization for moving object problem from time delay (TD) and Doppler frequency shift (DFS) measurements, where the transmitter position and velocity are unknown. The transmitter is not perfectly time syncronized such that unknown offsets exist in the TD and DFS measurements. We propose to jointly estimate the object and transmitter positions and velocities and the offsets. Using the TD and DFS measurements from both the indirect and direct paths between the transmitter and the receivers, we formulate a non-convex weighted least squares (WLS) problem. Local convergence may occur when solving the non-convex WLS problem, implying that good estimate is not guaranteed. Thus, we relax the non-convex WLS problem into a convex semidefinite program by applying semidefinite relaxation (SDR). Moreover, we theoretically show that the performance can be improved by using multiple transmitters as compared to that using single transmitter, although more unknown parameters are introduced. We then extend the proposed SDR method to handle the multiple transmitters case. Finally, the mean square error analysis is provided to show that the proposed WLS method reaches the Cramer-Rao lower bound accuracy under small Gaussian noise condition. Simulation results validate the theoretical analysis and show the superior performance over the existing methods.

HFR-Video-Based Stereo Correspondence Using High Synchronous Short-Term Velocities.

This study focuses on solving the correspondence problem of multiple moving objects with similar appearances in stereoscopic videos. Specifically, we address the multi-camera correspondence problem by taking into account the pixel-level and feature-level stereo correspondences, and object-level cross-camera multiple object correspondence. Most correspondence algorithms rely on texture and color information of the stereo images, making it challenging to distinguish between similar-looking objects, such as ballet dancers and corporate employees wearing similar dresses, or farm animals such as chickens, ducks, and cows. However, by leveraging the low latency and high synchronization of high-speed cameras, we can perceive the phase and frequency differences between the movements of similar-looking objects. In this study, we propose using short-term velocities (STVs) of objects as motion features to determine the correspondence of multiple objects by calculating the similarity of STVs. To validate our approach, we conducted stereo correspondence experiments using markers attached to a metronome and natural hand movements to simulate simple and complex motion scenes. The experimental results demonstrate that our method achieved good performance in stereo correspondence.

Analysis of Accelerometer Applications

An accelerometer is a digital tool that measures modifications in velocity, acceleration, or vibration of a bodily object. It is typically used in various packages, along with smartphones, vehicles, aerospace, and healthcare. The accelerometer measures the acceleration of the object via detecting modifications within the capacitance, piezoelectric impact, or other phenomena resulting from the motion of the item. Here we have studied and analysed various accelerometer applications to identify their capabilities and usability, based on this we have concluded that accelerometer can be used for the design and development of an Epilepsy Patient Fall Detection and Alert System using Acceleromete

Electromagnetic Doppler effect in non-reciprocal medium

Reflected light from a moving object, such as a mirror, is frequency-shifted via the Doppler effect. This well-known phenomenon is used to determine the speed and direction of an object due to the red or blue shifting of the received electromagnetic radiation frequency. To date, the Doppler theory has focused on the propagation of electromagnetic radiation through reciprocal media where the transiting photons have the same speed, regardless of direction. However, optical birefringence exists in solid-state, liquid, and gas media, which results in spatially dependent, and vector field-dependent, propagation velocities. When combined with the Faraday effect, which is employed routinely in laboratory setups and present at galactic scales, non-reciprocal Doppler effects may occur. A non-reciprocal Doppler effect theory is derived showing frequency shift dependencies on the object velocity and the directional speed of light in the medium. This general Doppler theory is shown to simplify the canonical relations for the Doppler effect in systems without a directional dependence on light. In non-reciprocal systems, when the mirror velocity is much less than the speed of light, the general frequency shift relation simplifies to a dependency on the roundtrip average speed of light. This theory provides a basis for the application of the Doppler effect to estimate the velocity of moving objects in non-reciprocal systems.

Computational Ghost Rotational Doppler Metrology

We present a scheme for measuring a rotational Doppler frequency shift with only a single-shot measurement. Our protocol benefits from the combination of computational temporal ghost imaging and heterodyne detection of the rotational Doppler effect. Specifically, by multiplexing a series of elaborately designed ring-shaped spatial patterns, we map the temporal rotational Doppler signal into the spatial domain and thus can collect the Doppler signal with a camera in a single-shot exposure. Then, by calculating intensity correlations between the resulting elaborate spatial patterns and the image captured by the camera, we reconstruct the temporal Doppler signal. In our experiments, we succeed in demonstrating fairly good performance for measuring the angular velocity of a real spinning object with a rough surface. Our scheme could find potential applications in remote sensing and could also inspire more attempts to develop protocols for temporal detection and processing on the basis of space-time duality.