Distance Estimation Research Articles

GENETIC DISTANCE AND IDENTITY AT IGF-1 GENE LOCUS OF SOME INDIGENOUS CATTLE BREEDS IN NIGERIA

An experiment was conducted to determine the genetic distance and identity at insulin-like growth factor 1 (IGF-1) gene locus in some indigenous cattle breeds in Nigeria. One (1) Local Government Area (LGA) each from Adamawa, Taraba, Gombe and Sokoto States was purposively selected which include Mubi North, Yorro, Balanga, and Wurno Local Government Areas, respectively. The snowball method was used to sample (96) cattle consisting of (24) Adamawa Gudali, (24) Sokoto Gudali, (24) Bunaji and (24) Rahaji cattle from pastoralists in many communities of the LGAs. Blood samples were collected from the animals through jugular venepuncture. DNA extraction was done using Zymo Quick DNA™ Mini prep kit. NanoDrop 1000 spectrophotometer was used for the determination of DNA quality and quantity using DNA purification kit. Genetic distances among the breeds of cattle were calculated using distance measures with the aid of Gen AIEx software package. The highest level of genetic distance estimate was observed between Rahaji and Bunaji cattle breeds (0.085) while the closest genetic distance (0.001) was observed between Sokoto Gudali and Bunaji. The highest genetic identities were recorded between Sokoto Gudali and Bunaji (0.999) cattle breeds whereas the closest matrix of genetic identity (0.904) was recorded between Rahaji and Sokoto Gudali cattle. Cluster analysis using UPMGA revealed that Bunaji and Sokoto Gudali cattle formed the first cluster which is separate from Adamawa, which formed the second cluster. Rahaji cattle formed the third cluster, which is far and distant from the first and second clusters at IGF-1 gene locus. Rahaji cattle had the longest genetic distance with other three breeds of cattle. High genetic distance and identity existed among some indigenous breeds of cattle studied.

The relationship between interpersonal distance preference and estimation accuracy in autism.

People naturally seek an interpersonal distance that feels comfortable, striking a balance between not being too close or too far from others until reaching a state of equilibrium. Previous studies on interpersonal distance preferences among autistic individuals have yielded inconsistent results. Some show a preference for greater distance, while others indicate a preference for shorter distances, or reveal higher variance in preferences among autistic individuals. In a related vein, previous studies have also investigated the way autistics accurately judge distance, and these studies have received inconsistent results, with some showing superior spatial abilities and others indicating biases in distance estimations. However, the link between distance estimation and preference has never been examined. To address this gap, our study measured interpersonal distance preferences and estimations and tested the correlation between the two factors. The results indicate greater variance among autistic people in both the preference of distance and the ability to estimate distance accurately, suggesting that inconsistencies in previous studies originate from greater individual differences among autistics. Furthermore, only among autistic individuals were interpersonal distance preference and estimation bias associated in a manner that violated equilibrium. Underestimation bias (judging others as closer than they are) was linked to a preference for closer proximity, while overestimation bias (judging others as further away) was associated with a preference for maintaining a greater distance. This connection suggests that biases in the estimation of interpersonal distance contribute to extreme preferences (being too close or too far away). Taken together, the findings suggest that biases in the estimation of interpersonal distance are associated with socially inappropriate distance preferences among autistics.

A Roadside Precision Monocular Measurement Technology for Vehicle-to-Everything (V2X).

Within the context of smart transportation and new infrastructure, Vehicle-to-Everything (V2X) communication has entered a new stage, introducing the concept of holographic intersection. This concept requires roadside sensors to achieve collaborative perception, collaborative decision-making, and control. To meet the high-level requirements of V2X, it is essential to obtain precise, rapid, and accurate roadside information data. This study proposes an automated vehicle distance detection and warning scheme based on camera video streams. It utilizes edge computing units for intelligent processing and employs neural network models for object recognition. Distance estimation is performed based on the principle of similar triangles, providing safety recommendations. Experimental validation shows that this scheme can achieve centimeter-level distance detection accuracy, enhancing traffic safety. This approach has the potential to become a crucial tool in the field of traffic safety, providing intersection traffic target information for intelligent connected vehicles (ICVs) and autonomous vehicles, thereby enabling V2X driving at holographic intersections.

On the use of field RR Lyrae as Galactic probes

Context. The Vera C. Rubin Observatory will start operations in 2025. During its first two years, too few visits per target per band will be available, meaning that the mean magnitude measurements of variable stars will not be precise and thus standard candles such as RR Lyrae (RRL) will not be usable. Light curve templates (LCTs) can be adopted to estimate the mean magnitude of a variable star with a few magnitude measurements, provided that their period (plus the amplitude and reference epoch, depending on how the LCT is applied) is known. The LSST will provide precise RRL periods within the first six months, enabling exploitation of RRLs if LCTs are available. Aims. We aim to build LCTs in the LSST bands to enhance the early science with LSST. Using them will provide a one- to two-year advantage with respect to the classical approach concerning distance measurements. Methods. We collected grί-band data from the ZTF survey and z-band data from DECam to build the LCTs of RRLs. We also adopted synthetic grίz band data in the LSST system from pulsation models, plus SDSS, Gaia and OGLE photometry, inspecting the light amplitude ratios in different photometric systems to provide useful conversions to apply the LCTs. Results. We have built LCTs of RRLs in the grίz bands of the LSST photometric system; for the z band, we could build only fun damental mode RRL LCTs. We quantitatively demonstrated that LCTs built with ZTF and DECam data can be adopted on the LSST photometric system. The LCTs will decrease the uncertainty on distance estimates of RRLs by a factor of at least two with respect to a simple average of the available measurements. Finally, within our tests, we have found a brand new behavior of amplitude ratios in the Large Magellanic Cloud.

Data‐Knowledge Driven Hybrid Deep Learning for Earthquake Early Warning

AbstractEarthquake early warning (EEW) is of great significance in mitigating seismic disasters. Traditional EEW algorithms, which are knowledge‐driven approaches, rely on seismologists' analysis. The limited intensity measures were extracted by seismologists from P‐wave signals. And there is considerable uncertainty for predicting epicentral distance, magnitude, peak ground acceleration (PGA), and peak ground velocity (PGV). Currently, data‐driven deep learning methods with the strong learning abilities do not consider knowledge information from seismologists in EEW; thus, there is unexplored potential in enhancing the performance of deep learning models for EEW. Here, we construct the Data‐knowledge driven Hybrid deep Learning network (DHLnet) for EEW using the waveform input, knowledge embedding, convolutional neural network and graph convolutional network, aiming to integrate knowledge information from knowledge‐driven methods and the strong learning ability of data‐driven deep learning methods, that is, improving the performance of EEW. For the same test data set, compared with knowledge‐driven methods and data‐driven deep learning models, we demonstrate that DHLnet enhances the timeliness and robustness in predicting the epicentral distance, magnitude, PGA, and PGV during 10 s time window following the arrival of P‐wave. Furthermore, to validate the generalization and robustness of the DHLnet in EEW, we applied the trained DHLnet to an independent data set, within first few seconds after an earthquake occurs, DHLnet can provide robust magnitude estimation, epicentral distance estimation and high alarm accuracy. The potential of the proposed network is to enhance the performance of EEW systems and provides new insights into the exploration of deep learning methods for EEW domain.

An Optimization Method for Location-Routing of Cruise Ship Cabin Materials Considering Obstacle Blocking Effects

This study examines the challenges related to the storage and distribution of cabin materials on board during the construction of cruise ships. The construction process of cruise ships involves the assembly of various cabin materials, which are temporarily stored and transported in a complex deck environment with multiple obstacles. These processes can be viewed as location-routing problems (LRPs). However, existing solutions for LRPs do not account for the obstacles commonly encountered in deck environments. Therefore, this paper introduces a new variant of LRPs, termed as the BE-LRP (blocking environment location-routing problem). Initially, by considering the optimization objectives of minimizing the distribution distance and the number of vehicles required, the paper develops an optimization model for the location-routing of on-board materials while taking obstacles into consideration. Subsequently, a method for estimating blocked distances is proposed. This method utilizes Gaussian process regression to predict blocked distances and enables the rapid estimation of distances obstructed by obstacles between different destinations. Lastly, a hybrid obstacle blocking distance processing HO-NSGAII algorithm is formulated to address the BE-LRP. Experimental comparisons demonstrate that the distribution scheme derived from this research method does not necessitate modifications to the delivery distance when compared to the traditional NSGAII algorithm, and better aligns with the actual deck conditions. With an equivalent number of delivery vehicles, the scheme results in shorter delivery distances. The delivery distance can be reduced by 3–17% under varying circumstances. These findings are advantageous for enhancing the efficiency of cruise ship construction and mitigating the impact of warehousing and distribution on construction progress.

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.

Machine-learning-based ensemble regression for vehicle-to-vehicle distance estimation using a toe-in style stereo camera

Adjusting the following distance from the front vehicle in highway traffic is important to reduce the risk of collision. Distance estimation is an important research area for advanced driver assistance systems. Therefore, this paper presents a methodology that combines the strengths of several machine learning algorithms using joint decision mechanisms and searches for optimal results for vehicle-to-vehicle distance estimation. The hyperparameter optimization of machine learning models is performed by an iterative algorithm that compares combinations of hyperparameter values. In addition, machine learning algorithms are combined and tested with ensemble learning methods to improve the results obtained. According to the experiments, the ensemble voting regression created by combining extreme gradient boosting, categorical boosting and two multi-layer perceptron models achieves the best result with a mean absolute percentage error value of 1.5444. Considering the comparisons made with other methods, the accuracy of the results obtained with the proposed method is quite high.Code is at: https://github.com/ozgurduran/V2V-VR.

DV-Hop Localization Based on Distance Estimation Using Multinode and Hop Loss in IoT

DV-Hop Localization Based on Distance Estimation Using Multinode and Hop Loss in IoT

An improved DVHop localization algorithm using a novel angle based node reduction and optimization technique

Wireless sensor networks are becoming increasingly popular across a range of applications. One notable use is in seismic exploration and monitoring for oil and gas reservoirs. This application involves deploying numerous sensor nodes across outdoor fields to measure backscattered waves, which are then used to create an image of the subsurface. These sensor nodes remain active in the field for several days and must be accurately localized to ensure efficient reservoir detection. However, the Distance Vector-Hop (DVHop) algorithm, despite its simplicity, is not suitable for accurate node localization in exploration fields due to obstructions. In this paper, we propose a modified DVHop algorithm specifically designed for precise localization in such environments. Proposed algorithm uses angles between intermediate nodes to identify and circumvent nodes affected by obstructions. Distance estimation is performed using this reduced set of nodes. The estimated distances between these nodes are subsequently solved using Velocity Pausing Particle Swarm Optimization to determine the nodes’ locations. When evaluated in environments resembling exploration fields, our algorithm demonstrated an improvement of 25% to 63% in Average Localization Accuracy compared to other hop-based localization algorithms under similar conditions.

The cat is out of the bag: the effect of virtual characters and scale cues on physics coherence

According to previous research, humans are exceptionally skilled at distinguishing earth-congruent object trajectories from earth-discrepant ones. Inconsistent scale cues in Virtual Reality (VR), however, are shown to easily confuse VR users’ scale, size, and distance estimations. This paper describes the results of two studies, with 40 participants each, which tested the effects of self-scaling, and size cues from active virtual characters, on perceived physics coherence (i.e., subjective realism). In the first study (Study A), a humanoid robot the size of a doll manipulated household objects to demonstrate their rigid body dynamics. The demonstration sequence was performed twice, once using a correct approximation of physics, and once using an incorrect one in which gravity was simulated similarly to as if the doll-sized robot was human-sized and the surrounding room was enlarged. The participants observed both demonstrations, once while standing at the normal scale and once at a reduced scale, similar to that of the humanoid robot. The second study (Study B) was similar, except that the virtual character demonstrating the physics was a regular-sized cat. Our preregistered hypotheses predicted that participants would consider the correct approximation of physics as better matching their expectations at normal scale, and the incorrect one as better matching their expectations at the reduced scale. However, only the second of these hypotheses was supported. According to our exploratory analyses, the participant’s own scale was a poor predictor of physics preference, and instead there was a significant effect regarding the virtual character’s identity. Participants observing the virtual cat were about eight times more likely to select the realistic physics model compared to those observing the humanoid robot. The results indicate that familiar cues tied to virtual character identity overrode any potential effects related to changes in the participants’ own scale.

The influence of affective voice on sound distance perception.

Affective stimuli in our environment indicate reward or threat and thereby relate to approach and avoidance behavior. Previous findings suggest that affective stimuli may bias visual perception, but it remains unclear whether similar biases exist in the auditory domain. Therefore, we asked whether affective auditory voices (angry vs. neutral) influence sound distance perception. Two VR experiments (data collection 2021-2022) were conducted in which auditory stimuli were presented via loudspeakers located at positions unknown to the participants. In the first experiment (N = 44), participants actively placed a visually presented virtual agent or virtual loudspeaker in an empty room at the perceived sound source location. In the second experiment (N = 32), participants were standing in front of several virtual agents or virtual loudspeakers and had to indicate the sound source by directing their gaze toward the perceived sound location. Results in both preregistered experiments consistently showed that participants estimated the location of angry voice stimuli at greater distances than the location of neutral voice stimuli. We discuss that neither emotional nor motivational biases can account for these results. Instead, distance estimates seem to rely on listeners' representations regarding the relationship between vocal affect and acoustic characteristics. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Impact of Perception Errors in Vision-Based Detection and Tracking Pipelines on Pedestrian Trajectory Prediction in Autonomous Driving Systems.

Pedestrian trajectory prediction is crucial for developing collision avoidance algorithms in autonomous driving systems, aiming to predict the future movement of the detected pedestrians based on their past trajectories. The traditional methods for pedestrian trajectory prediction involve a sequence of tasks, including detection and tracking to gather the historical movement of the observed pedestrians. Consequently, the accuracy of trajectory prediction heavily relies on the accuracy of the detection and tracking models, making it susceptible to their performance. The prior research in trajectory prediction has mainly assessed the model performance using public datasets, which often overlook the errors originating from detection and tracking models. This oversight fails to capture the real-world scenario of inevitable detection and tracking inaccuracies. In this study, we investigate the cumulative effect of errors within integrated detection, tracking, and trajectory prediction pipelines. Through empirical analysis, we examine the errors introduced at each stage of the pipeline and assess their collective impact on the trajectory prediction accuracy. We evaluate these models across various custom datasets collected in Taiwan to provide a comprehensive assessment. Our analysis of the results derived from these integrated pipelines illuminates the significant influence of detection and tracking errors on downstream tasks, such as trajectory prediction and distance estimation.

Impact of Antenna Orientation on Localization Accuracy Using RSSI-based Trilateration

The goal of the indoor localization is to determine the position and orientation of people, devices, and mobile robots. With the rise of Industry 4.0, wireless communication technologies have emerged as a rapidly evolving and crucial area for achieving this goal. Various radiocommunication-based technologies, including Bluetooth, Bluetooth Low Energy (BLE), Wi-Fi, Ultra-Wideband (UWB), and ZigBee offer means to indirectly estimate distance. These methods leverage diverse principles such as time-based measurements, signal strength, and angle of arrival. Indoor positioning systems can be categorized into two approaches: distance-based and distance-independent techniques. The Free Space Path Loss (FSPL) model describes the connection between distance and Received Signal Strength Indicator (RSSI). The parameters within this model significantly impact distance estimation and localization accuracy. Therefore, a method that accurately characterizes the model is critical. This work proposes an orientation-based localization technique utilizing RSSI and trilateration. Measurements were conducted between two ESP32 units in various orientations to obtain optimal parameters for each specific scenario. To assess the effectiveness of this approach, two scenarios were evaluated: one considering orientation and another neglecting it. The results show that incorporating orientation information leads to significantly more accurate positioning compared to the orientation-agnostic approach.

AI based Online Proctoring Remote Monitoring Intruder, Emotion Detection and Distance Estimation

AI based Online Proctoring Remote Monitoring Intruder, Emotion Detection and Distance Estimation

Object Detection and Monocular Stable Distance Estimation for Road Environments: A Fusion Architecture Using YOLO-RedeCa and Abnormal Jumping Change Filter

Enabling rapid and accurate comprehensive environmental perception for vehicles poses a major challenge. Object detection and monocular distance estimation are the two main technologies, though they are often used separately. Thus, it is necessary to strengthen and optimize the interaction between them. Vehicle motion or object occlusions can cause sudden variations in the positions or sizes of detection boxes within temporal data, leading to fluctuations in distance estimates. So, we propose a method to integrate a detector based on YOLOv5-RedeCa, a Bot-Sort tracker and an anomaly jumping change filter. This combination allows for more accurate detection and tracking of objects. The anomaly jump filter smooths distance variations caused by sudden changes in detection box sizes. Our method increases accuracy while reducing computational demands, showing outstanding performance on several datasets. Notably, on the KITTI dataset, the standard deviation of the continuous ranging results remains consistently low, especially in scenarios with multiple object occlusions or disappearances. These results validate our method’s effectiveness and precision in managing dual tasks.

Atomistic descriptor optimization using complementary Euclidean and geodesic distance information

Descriptors are physically-inspired, symmetry-preserving schemes for representing atomistic systems that play a central role in the construction of models of potential energy surfaces. Although physical intuition can be flexibly encoded into descriptor schemes, they are generally ultimately guided only by the spatial or topological arrangement of atoms in the system. However, since interatomic potential models aim to capture the variation of the potential energy with respect to atomic configurations, it is conceivable that they would benefit from descriptor schemes that implicitly encode both structural and energetic information rather than structural information alone. Therefore, we propose a novel approach for the optimisation of descriptors based on encoding information about geodesic distances along potential energy manifolds into the hyperparameters of commonly used descriptor schemes. To accomplish this, we combine two ideas: (1) a differential-geometric approach for the fast estimation of approximate geodesic distances [Zhu et al., J. Chem. Phys. 150, 164103 (2019)]; and (2) an information-theoretic evaluation metric – information imbalance – for measuring the shared information between two distance measures [Glielmo et al. PNAS Nexus, 1, 1 (2022)]. Using three example molecules – ethanol, malonaldehyde, and aspirin – from the MD22 dataset, we first show that Euclidean (in Cartesian coordinates) and geodesic distances are inequivalent distance measures, indicating the need for updated ground-truth distance measures that go beyond the Euclidean (or, more broadly, spatial) distance. We then utilize a Bayesian optimisation framework to show that descriptors (in this case, atom-centred symmetry functions) can be optimized to maximally express a certain type of distance information, such as Euclidean or geodesic information. We also show that modifying the Bayesian optimisation algorithm to minimise a combined objective function – the sum of the descriptor ↔ Euclidean and descriptor ↔ geodesic information imbalances – can yield descriptors that not only optimally express both Euclidean and geodesic distance information simultaneously, but in fact resolve substantial disagreements between descriptors optimized to encode only one type of distance measure. We discuss the relevance of our approach to the design of more physically rich and informative descriptors that can encode useful, alternative information about molecular systems.

GENETIC DISI'ANCE MEASURES AMONG FIVE INDIGENOUS CATTLE BREEDS IN NIGERIA

Genetic variation is the basis of effective improvement in farm animals. Population differentiation is used for objective choice of parental genotypes that constitutes new hybrids in crossbreeding. In Nigeria, population characteristics of selected indigenous cattle breeds have not been fully documented. Therefore, blood protein electrophoretic patterns of selected indigenous cattle breeds in Nigeria were assessed. Blood samples (5mL) were taken underneath the tail by venipuncture from 200 cattle (40/breed) randomly selected from the five indigenous cattle breeds. The samples were subjected to cellulose acetate electrophoresis to determine the genetic variants of haemoglobin (Hb), carbonic anhydrase (CA) and transferrin (TO. Data were analysed using Euclidean genetic distance and cluster analysis. Two main clusters from the dendrogram were observed for each of Hb, CA and TE Euclidean genetic distance at the Hb locus between WF and SG, WF and RB, WF and BK, WF and Muturu were 15.3, 15.7, 23.9 and 35.1 respectively, while the corresponding values at CA locus were 3.9, 3.3, 29.1 and 36.2, similarly, the corresponding values at Tf locus were 22.8, 25.8, 94.3 and 102.7. Genetic distance estimates were found to be higher between non-related breeds than between breeds which had common ancestors.

A Vehicle Monocular Ranging Method Based on Camera Attitude Estimation and Distance Estimation Networks

A monocular ranging method for forward vehicles in intelligent driving is proposed. This method measures vehicle distance more accurately under the condition of a single camera and can estimate camera attitude in real-time. For the estimation of camera pitch and yaw angles, it is achieved using road vanishing points. The images collected by the camera are sequentially processed through the Roberts operator amplitude calculation, feature point extraction, feature line segment generation, road vanishing point voting, and estimation of camera attitude to obtain pitch and yaw angles. A distance estimation network was designed, which is divided into multiple levels based on image size and incorporates image feature, integrating vehicle grounding point, and vehicle width information, effectively improving ranging accuracy. Finally, validation was conducted on KITTI data, with a relative error (AbsRel) of 8.3%. Additionally, the TuSimple dataset and continuous driving scenarios were also validated, resulting in improved performance compared to previous algorithms.

LADDER: Revisiting the Cosmic Distance Ladder with Deep Learning Approaches and Exploring Its Applications

We investigate the prospect of reconstructing the “cosmic distance ladder” of the Universe using a novel deep learning framework called LADDER—Learning Algorithm for Deep Distance Estimation and Reconstruction. LADDER is trained on the apparent magnitude data from the Pantheon Type Ia supernova compilation, incorporating the full covariance information among data points, to produce predictions along with corresponding errors. After employing several validation tests with a number of deep learning models, we pick LADDER as the best-performing one. We then demonstrate applications of our method in the cosmological context, including serving as a model-independent tool for consistency checks for other data sets like baryon acoustic oscillations, calibration of high-redshift data sets such as gamma-ray bursts, and use as a model-independent mock-catalog generator for future probes. Our analysis advocates for careful consideration of machine learning techniques applied to cosmological contexts.