Distance Estimation Research Articles

The impact of camera-monitor system viewing angles on drivers’ distance perception: A simulated driving study

Camera-monitor systems (CMS) are increasingly used in driving. CMS separates the driver's sight line from the camera view, due to the lack of mirror reflection, only changing the camera's visual axis angle may affect the driver's rear view perception. While previous research has explored camera height and field of view, the effects of horizontal and vertical viewing angles alone remain unclear. This study aimed to evaluate how the horizontal viewing angle and vertical viewing angle of CMS camera affect distance estimation and car-following tasks. By changing the horizontal and vertical viewing angle, different self-vehicle references and horizon positions were formed in the image. Two experiments were conducted with the CMS around the steering wheel (Experiment 1) and at the bottom of the A-pillar (Experiment 2). Independent variables were the horizontal viewing angle (reference scale: 1/4, 1/3, 1/2) and vertical viewing angle (horizon position: 1/2, 1/3). Dependent variables included distance estimation error ratio and following distance. Experiment 1 demonstrated a significant interaction effect: a smaller reference scale and higher horizon position reduced distance underestimation. Additionally, a smaller reference scale for the participants' self-vehicle resulted in shorter following distances. In Experiment 2, the distance estimation outcomes on the left display aligned with those of Experiment 1; however, the influence of the viewing angle was diminished on the right display. The study suggests CMS design should balance vehicle reference inclusion with environmental cues, enhancing distance perception and driving safety. The consistency between CMS design and driver familiarity also needs to be considered.

Standard Errors for Calibrated Parameters

Abstract Calibration, the practice of choosing the parameters of a structural model to match certain empirical moments, can be viewed as minimum distance estimation. Existing standard error formulas for such estimators require a consistent estimate of the correlation structure of the empirical moments, which is often unavailable in practice. Instead, the variances of the individual empirical moments are usually readily estimable. Using only these variances, we derive conservative standard errors and confidence intervals for the structural parameters that are valid even under the worst-case correlation structure. In the over-identified case, we show that the moment weighting scheme that minimizes the worst-case estimator variance amounts to a moment selection problem with a simple solution. Finally, we develop tests of over-identifying or parameter restrictions. We apply our methods empirically to a model of menu cost pricing for multi-product firms and to a heterogeneous agent New Keynesian model.

Quarta: quantum supervised and unsupervised learning for binary classification in domain-incremental learning

Quantum machine learning recently gained prominence due to the promise of quantum computers in solving machine learning problems that are intractable on a classical computer. Nevertheless, several studies on problems which remain challenging for classical computing algorithms are emerging. One of these is classifying continuously incoming data instances in incremental fashion, which is studied in this paper through a hybrid computational solution that combines classical and quantum techniques. Hybrid approaches represents one of the current ways for the use of quantum computation in practical applications. In this paper, we show how typical issues of domain-incremental learning can be equally addressed with the properties of quantum mechanics, until to offer often better results. We propose the framework QUARTA to combine algorithms of quantum supervised learning, that is, variational quantum circuits, and techniques used in quantum unsupervised learning, that is, distance estimation. We aim at keeping the classification capabilities, which have learned on previously processed data instances, preserved as much as possible, and then acquiring new knowledge on new data instances. Experiments are performed on real-world datasets with quantum simulators.

Exploring faint white dwarfs and the luminosity function with Subaru HSC and SDSS in Stripe 82

Abstract We present 5,080 white dwarf (WD) candidates selected from stars matching between the multi-band imaging datasets of the Subaru Hyper Suprime-Cam (HSC) Survey and the Sloan Digital Sky Survey (SDSS) in the Stripe82 region covering about 165 deg2. We select WD candidates from the “reduced proper motion” diagram by combining the apparent magnitude in the range i = 19 – 24 and the proper motion measured from the datasets among a baseline of ∼ 14 years. We refine the WD candidates by fitting blackbody and template WD atmosphere models to HSC photometries for each candidate, enabling the estimation of distance and tangential velocity (vt). The deep HSC data allow us to identify low-temperature (<4000 K) and faint WD candidates down to absolute magnitude, Mbol ≃ 17. We evaluate the selection function of our WD candidates using a mock catalogue of spatial and kinematic distributions of WDs in the (thin and thick) disc and halo regions based on a Galactic model. We construct samples of disc and halo WD candidates by selecting WDs with tangential velocity, 40 < vt/[km s−1] < 80 and 200 < vt/[km s−1] < 500, respectively. The total number densities of the disc and halo WDs are (9.33 ± 0.89) × 10−3 pc−3 and (6.34 ± 2.90) × 10−4 pc−3. Our luminosity functions (LF) extend down to fainter absolute magnitudes compared with previous work. The faint WDs could represent the oldest generation of building blocks over the past ∼10 billion years of the assembly history of our Milky Way.

Multi-line laser 3D reconstruction method based on spatial quadric surface and geometric estimation

The traditional multi-line laser 3D reconstruction is based on binocular epipolar constraints and the equation of the laser optical plane. Firstly, matching points are identified based on the epipolar constraints. Then, the correct matching points are selected based on the optical plane equation of the multi-line laser. Finally, 3D reconstruction is performed using the selected matching points. In actual production processes involving multi-line lasers, noticeable distortion occurs due to the projection of Diffractive Optical Elements (DOE) at a large angle. This results in curved laser light planes and curved reflections on the measured objects. Under such circumstances, efficiently screening matching points using the traditional method based on the laser plane equation becomes challenging. Additionally, inherent noise in multi-line laser systems introduces errors in extracted laser center coordinates, making it impossible to directly obtain high-precision 3D data. To address these issues, this paper proposes a method that utilizes spatial quadric surfaces and geometric estimation for completing multi-line laser 3D reconstructions. By analyzing the distortion principle of DOE and the positional relationship after optical diffraction, the multi-line laser manifests as a quadric surface on the optical output plane. Consequently, by calibrating the equations of the quadric surface and applying binocular polar constraints, suitable matching points for the multi-line laser can be chosen. Once an accurate matching point is identified, a minimum geometric distance estimation can be established based on the distance constraint between the point and its corresponding epipolar line. This distance represents the separation between the left and right camera’s laser center points and their respective epipolar lines. Utilizing this estimate of the minimum geometric distance, a refined calculation can be performed to better satisfy epipolar constraints and obtain new matching points. Ultimately, utilizing these new matching points enables the completion of 3D reconstruction for multi-line lasers. In comparison with methods relying on spatial plane and epipolar constraints, our algorithm exhibits a superior degree of matchability and accuracy.According to multiple groups of test data, the average error before optimization is 0.3126 mm, and can be increased to 0.0336 mm after optimization.

CNN for scalar-source distance estimation in grid-generated turbulence

Countermeasures against material leakage in industrial plants call for rapid leak source estimation techniques based on limited downstream information. Assuming that such scalar diffusion occurs in a turbulent environment, it is necessary to extract features from its instantaneous local concentration distribution and estimate the distance from the observation point to the source. To this end, we proposed an estimation method using a supervised deep learning of a convolutional neural network (CNN) and investigated its estimation performance in grid-generated turbulence fields. The grid turbulence fields were reproduced by direct numerical simulations at three Reynolds numbers (Re), and the CNN was trained using two-dimensional distribution images of passive scalars downstream. We confirmed that the images large enough to cover the spatial integral length scales of the scalar resulted in high estimation accuracy. For images with a coarsened brightness gradation, the small-scale feature of scalar fluctuations was lost, and the CNN attained a low estimation accuracy. By training the CNN with high-Re images, a high estimation accuracy was obtained, even for low-Re images. The opposite case (low-Re training and high-Re estimation) yielded low accuracy, where the CNN significantly underestimated the distance against the source. Based on these results, we discussed the generalizability and essential features to be learned by the CNN.

AstroSat’s view of 4U 1735−44: spectral, temporal, and type I X-ray burst studies

ABSTRACT This study utilizes the simultaneous broad-band observations of 4U 1735−44 from AstroSat, offering enhanced spectral and temporal resolution, to investigate its spectral properties, temporal behaviour, and burst characteristics. Spectral, type I X-ray burst, and temporal analyses on 4U 1735−44 were performed using AstroSat/Soft X-ray Telescope and Large Area X-ray Proportional Counter (LAXPC) observations. The hardness–intensity diagram from LAXPC-20 showed a positive correlation between hardness and intensity, with a pattern resembling the banana branch typical of atoll sources. Spectral analysis carried out in the 0.7–20.0 keV energy range, using the model combination – $\tt {constant}$$\times$$\tt {tbabs}$ ($\tt {nthcomp}$$+$$\tt {diskbb}$$+$$\tt {bbodyrad}$), suggested a cool accretion disc truncated at a large distance from the neutron star in the system. Time-resolved spectral studies of two type I X-ray bursts detected from the source revealed evidence of photospheric radius expansion, allowing for an estimation of the source distance. Temporal analysis showed the presence of low-frequency quasi-periodic oscillation at $\sim$69 Hz (3.3$\sigma$ significance with more than 99 per cent confidence) and prominent noise features below 30 Hz.

UWB distance estimation errors in (non-)line of sight situations within the context of 3D analysis of human movement

Abstract Integrated Ultrawideband (UWB) and Magnetic Inertial Measurement Unit (MIMU) sensors are becoming popular for indoor localization applications, as a higher accuracy can be achieved than with just MIMU sensors. These integrated sensors could extend stability and accuracy in the field of 3D analysis of human movement (3D AHM) if they can deliver position estimates with an accuracy close to 1 cm. Achieving this high accuracy of 1 cm remains challenging, with most studies reporting position estimation errors around 5 cm, often due to Non-Line of Sight (NLOS) conditions and systematic UWB sensor distance estimation errors. Studying the distance error characteristic of UWB in situations of 3D AHM is essential to deal with these errors. While research on UWB distance errors in Line of Sight (LOS) and NLOS situations exists, few studies focus on the NLOS errors caused by the human body and were not in the relevant scenarios of 3D AHM. Therefore, this article examines UWB sensor performance and distance error characteristics in LOS and NLOS situations typical for the 3D AHM. Both the LOS and NLOS situations were studied in the typical 3D AHM distance range of 0.2 m to 2 m. The NLOS situations were studied first with a human subject as NLOS causing object and then with simulated human body segments (PVC pipes filled with water) of varying diameters. In LOS situations, consistent systematic bias errors were observed, along with incidental errors at specific positions in the room. In NLOS scenarios caused by the human and simulated body segments, a consistent and reproducible overestimation of distances was found. The reproducibility of these errors based on relative node and object positions suggests that systematic mitigation methods could significantly reduce errors, enabling more accurate and reproducible 3D human movement analysis.

Dynamic acoustic vowel distances within and across dialects.

Vowels vary in their acoustic similarity across regional dialects of American English, such that some vowels are more similar to one another in some dialects than others. Acoustic vowel distance measures typically evaluate vowel similarity at a discrete time point, resulting in distance estimates that may not fully capture vowel similarity in formant trajectory dynamics. In the current study, language and accent distance measures, which evaluate acoustic distances between talkers over time, were applied to the evaluation of vowel category similarity within talkers. These vowel category distances were then compared across dialects, and their utility in capturing predicted patterns of regional dialect variation in American English was examined. Dynamic time warping of mel-frequency cepstral coefficients was used to assess acoustic distance across the frequency spectrum and captured predicted Southern American English vowel similarity. Root-mean-square distance and generalized additive mixed models were used to assess acoustic distance for selected formant trajectories and captured predicted Southern, New England, and Northern American English vowel similarity. Generalized additive mixed models captured the most predicted variation, but, unlike the other measures, do not return a single acoustic distance value. All three measures are potentially useful for understanding variation in vowel category similarity across dialects.

A Note on the Fourier Magnitude Data and Sobolev Embeddings

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Near Real‐Time Earthquake Monitoring in Texas Using the Highly Precise Deep Learning Phase Picker

AbstractArtificial intelligence (AI) seismology has witnessed enormous success in a variety of fields, especially in earthquake detection and P and S‐wave arrival picking. It has become widely accepted that DL techniques greatly help routine seismic monitoring by enabling more accurate picking than traditional pickers like STA/LTA. However, a completely automatic AI‐driven earthquake monitoring framework has not been reported due to the concerns of potential false positives using DL pickers. Here, we propose a novel AI‐facilitated near real‐time monitoring framework using a recently developed deep learning (DL) picker (EQCCT) that has been deployed in the Texas seismological network (TexNet). For the West Texas area, TexNet's seismic monitoring relies on the EQCCT picker to report earthquake events. For earthquakes with a magnitude above two, the picks are further validated by analysts to output the final TexNet catalog. Due to the fast‐increasing seismicity caused by continuing oil&gas production in West Texas, this AI‐facilitated framework significantly relieves the workload of TexNet analysts. We show the mean absolute error (MAE) of automatic magnitude estimation for the magnitude‐above‐two earthquakes is smaller than 0.15 in West Texas and MAEs of hypocenter locations within 2.6 km in both distance and depth estimates. This research provides more evidence that DL pickers can play a fundamental role in daily earthquake monitoring.

Edy App: Earthquake Mitigation Innovation Using GIS for Disaster Response in Pariaman, Indonesia

Pariaman City, located along the Semangko fault line and the subduction zone of the Indo-Australian and Eurasian plates, is highly vulnerable to earthquakes and tsunamis. The region's susceptibility to large earthquakes, followed by vertical deformations, highlights the need for effective disaster mitigation. In response, this study proposes the development of the Earthquake Buddy Application (EDY App), designed to assist with earthquake mitigation efforts. The EDY App leverages Geographic Information System (GIS) technology to map earthquake-prone areas, provide early warnings, suggest evacuation routes and Temporary Evacuation Sites (TES), and facilitate aid distribution. This Android-based mobile application focuses on four key aspects: education, mitigation, evacuation, and donation. Key features include an Earthquake Vulnerability Map, real-time early warnings, mitigation instructions, TES recommendations with distance and time estimations, emergency contact options, and donation channels. Built with React Native, PHP, and MySQL, and integrated with Google Maps API, the app is designed to be user-friendly and accessible to the public. The EDY App’s contribution to earthquake mitigation lies in its ability to create a disaster-aware community. By offering comprehensive information and support during critical moments, the app can potentially reduce casualties and enhance community preparedness in earthquake-prone regions like Pariaman. Through this innovation, it is expected that residents will be better equipped to respond to natural disasters, promoting a culture of safety and resilience.

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.

AI-Enhanced Distance Estimation via Radio Chip Link Quality Metrics and Time-of-Flight Analysis With UWB Technology: A Comparative Evaluation

AI-Enhanced Distance Estimation via Radio Chip Link Quality Metrics and Time-of-Flight Analysis With UWB Technology: A Comparative Evaluation

Natural- and redirected walking in virtual reality: Spatial performance and user experience

AbstractImmersive virtual reality offers a range of unique possibilities. One of these is the realistic exploration of virtual worlds using natural walking. This however becomes difficult when the size of the virtual world exceeds that of the available physical space. Redirected walking in virtual reality presents a novel solution to this problem by typically making its users think to be walking in a straight line while they are in fact walking in a curve, thus allowing them to physically walk long distances in confined physical spaces. Yet, few studies have examined the effects of redirected walking on variables such as spatial memory, navigation and user experience as compared to other immersive and non-immersive locomotion methods. In a maze task we examined 1) redirected- and 2) natural walking in immersive virtual reality conditions, and 3) artificial locomotion on a non-immersive desktop monitor. Walked path lengths became shorter and distance estimates, object location memory and user experience improved using natural walking compared to a monitor condition. However, redirected walking yielded similar performance to natural walking while requiring less physical space, opening up possibilities for more pervasive use of real locomotion in virtual environments.

A Robust Routing Protocol in Cognitive Unmanned Aerial Vehicular Networks.

The adoption of UAVs in defence and civilian sectors necessitates robust communication networks. This paper presents a routing protocol for Cognitive Radio Unmanned Aerial Vehicles (CR-UAVs) in Flying Ad-hoc Networks (FANETs). The protocol is engineered to optimize route selection by considering crucial parameters such as distance, speed, link quality, and energy consumption. A standout feature is the introduction of the Central Node Resolution Factor (CNRF), which enhances routing decisions. Leveraging the Received Signal Strength Indicator (RSSI) enables accurate distance estimation, crucial for effective routing. Moreover, predictive algorithms are integrated to tackle the challenges posed by high mobility scenarios. Security measures include the identification of malicious nodes, while the protocol ensures resilience by managing multiple routes. Furthermore, it addresses route maintenance and handles link failures efficiently, cluster formation, and re-clustering with joining and leaving new nodes along with the predictive algorithm. Simulation results showcase the protocol's self-comparison under different packet sizes, particularly in terms of end-to-end delay, throughput, packet delivery ratio, and normalized routing load. However, superior performance compared to existing methods, particularly in terms of throughput and packet transmission delay, underscoring its potential for widespread adoption in both defence and civilian UAV applications.

Novel calibration method for improved UWB sensor distance measurement in the context of application for 3D analysis of human movement

Integrated UWB and MIMU sensor systems have become popular for pedestrian tracking and indoor localization, since this facilitates data fusion that improves position estimation accuracy by exploiting the complementary nature of their error sources. Integrated UWB/MIMU sensors also have great potential in only on-body use for 3D analysis of human movement, as with MIMU sensors alone accurate direct estimation of (relative) body segment position is not possible. For this, a position estimation accuracy with errors smaller than 1 cm is deemed required. The lowest position estimating error with integrated UWB/MIMU systems, reported so far, is around 5 cm. The main accuracy limiting factors were found to be the systematic errors in the distance estimates from the UWB sensor. Multiple reported attempts to calibrate for these systematic errors failed to achieve the desired accuracy. This article presents a novel distance-bias calibration method that minimizes the residual systematic distance estimate errors using multiple sensors in a swarm configuration. Validation was performed against synthetic reference data and against reference data measured with an optical motion tracking system. Significantly reduced systematic distance estimate errors (≤0.5 cm) were found. These results promise to facilitate significantly better position estimates in future UWB/MIMU data fusion.

Perceived travel distance depends on the speed and direction of self-motion.

Although estimating travel distance is essential to our ability to move through the world, our distance estimates can be inaccurate. These odometric errors occur because people tend to perceive that they have moved further than they had. Many of the studies investigating the perception of travel distance have primarily used forward translational movements, and postulate that perceived travel distance results from integration over distance and is independent of travel speed. Speed effects would imply integration over time as well as space. To examine travel distance perception with different directions and speeds, we used virtual reality (VR) to elicit visually induced self-motion. Participants (n = 15) were physically stationary while being visually "moved" through a virtual corridor, either judging distances by stopping at a previously seen target (Move-To-Target Task) or adjusting a target to the previous movement made (Adjust-Target Task). We measured participants' perceived travel distance over a range of speeds (1-5 m/s) and distances in four directions (up, down, forward, backward). We show that the simulated speed and direction of motion differentially affect the gain (perceived travel distance / actual travel distance). For the Adjust-Target task, forwards motion was associated with smaller gains than either backward, up, or down motion. For the Move-To-Target task, backward motion was associated with smaller gains than either forward, up or down motion. For both tasks, motion at the slower speed was associated with higher gains than the faster speeds. These results show that transforming visual motion into travel distance differs depending on the speed and direction of optic flow being perceived. We also found that a common model used to study the perception of travel distance was a better fit for the forward direction compared to the others. This implies that the model should be modified for these different non-forward motion directions.

Localisation for underwater acoustic networks with a piece-wise linear sound speed profile

ABSTRACT Underwater agent localisation is a precondition for plenty of ocean applications. The variation of the underwater sound speed (USS) is one of the main factors that constrain the performance of underwater localisation. In this paper, a scheme is designed to reduce the agent localisation error arising from the USS variation in deep water and an agent localisation algorithm named ‘localisation with a piecewise linear USS' (LPL) is proposed. According to the characteristics of a real USS in deep water, a piecewise linear USS model is introduced to approximate the real USS. The agent above under the ‘sound fixing and ranging’ (SOFAR) channel are both considered. The LPL algorithm consists of three parts: initial value estimation, horizontal distance estimation and localisation. Considering the piecewise linear USS profile, the proposed LPL algorithm improves the performance for localisation with 60% more precision than the state-of-the-art methods.

Rapid distance estimation of odor sources by electronic nose with multi-sensor fusion based on spiking neural network

The inference of odor source locations holds great potential value in chemical leak detection, explosive searches and flammable substance detection. Sensor response and derivative changes are common indicators used to estimate source distance. However, most studies often rely on single-feature methods using a single sensor. In this study, a novel distance indicator based on a spiking neural network (SNN) that integrates multiple sensors is proposed. SNN was introduced for effective multi-sensor data fusion, which can provide accurate and rapid source distance estimation. The indicator's accuracy and quick detection capabilities were tested using a publicly available wind tunnel dataset and compared with single-sensor indicators. Additionally, an olfactory robot platform was constructed to test the method's real-time performance. Results demonstrate that this method effectively utilizes sensor arrays to estimate source distance, with an average root mean square error (RMSE) of less than 0.1 m across different wind speeds, outperforming single-sensor-based indicators. Additionally, the model exhibits good distance estimation performance within a relatively short detection time, with an average RMSE of 0.118 m. Practical testing on a mobile robot platform confirms the applicability of this distance indicator for real-time distance monitoring. In conclusion, the proposed distance indicator enables effective distance estimation for odor sources, providing a promising method for odor source localization and olfactory navigation.