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Abstract Gene expression profile data have high-dimensionality with a small number of samples. These data characteristics lead to a long training time and low performance in predictive model construction. To address this issue, the paper proposes a feature selection algorithm using non-dominant feature-guide search. The algorithm adopts a filtering framework based on feature sorting and search strategy to overcome the problems of long training time and poor performance. First, the feature pre-selection is completed according to the calculated feature category correlation. Second, a multi-objective optimization feature selection model is constructed. Non-dominant features are defined according to the Pareto dominance theory. Combined with the bidirectional search strategy, the Pareto dominance features under the current category maximum relevance feature are removed one by one. Finally, the optimal feature subset with maximum correlation and minimum redundancy is obtained. Experimental results on six gene expression data sets show that the algorithm is much better than Fisher score, maximum information coefficient, composition of feature relevancy, mini-batch K-means normalized mutual information feature inclusion, and max-Relevance and Min-Redundancy algorithms. Compared to feature selection method based on maximum information coefficient and approximate Markov blanket, the algorithm not only has high computational efficiency but also can obtain better classification capabilities in a smaller dimension.

Abstract To locate the ground encroachments in transmission line corridor effectively, the application of a novel binocular stereo vision (BSV) for distance measurement is realized with improvement and adjustment in calibration and reconstruction process of BSV. Firstly, a calibration method using a 1‐D pole with spatial constraints to replace the 2‐D calibration object is proposed. The field experiment shows that this method improves the accuracy of the calibration in a large‐scale scene by expanding the calibration area. Then, a regulation algorithm to eliminate the difference between focal lengths for binocular cameras is designed to further improve accuracy of calibration. For practical utilization of BSV in the monitor system, the 3‐D reconstructions from world coordinate system (WCS) to ground‐based system is achieved by rotation and transformation of the reconstructed coordinate system and direct height measurement is realized consequently. Further, based on the calibration method being proposed, the partial calibration with a reduced calibration distribution area is studied considering the probable complex terrain. Height measurement calculations are carried out on both hypothetical and real obstacles to verify the validity of the calibration method and postprocessing algorithms. The results demonstrated that the calibration method algorithms successfully realized the practical application of BSV in large‐scale scene with high accuracy. By using this novel BSV technology, the total transmission line inspection cost is expected to be greatly reduced by comparing with manned helicopter and UAV inspections.

Smart agriculture utilizes a variety of advanced technologies to promote sustainable agriculture and provide solutions for intelligent, automated and unmanned agriculture. Agricultural robots and related technologies are an important part of smart agriculture, while autonomous navigation is a core function of autonomous agricultural robots, which rely on information about the distance of obstacles in a scene to support decision making. In this paper, we propose a ground point geometric ranging model, which can be used in camera height dynamic change scenarios, and the method is validated by model derivation and hypothesis testing. The model combines ranging and camera calibration, choosing to compensate for distortion and defocus phenomena caused by nonlinear imaging of the camera to the focal length, and completes the parameter calibration using a small amount of ground point real distance data. In this paper, the YOLOv8 model is used to identify and range outdoor cattle, and the experimental results show that the lowest range accuracy of this method reaches 95%, this method eliminates the dependence on camera height for focal calibration in ranging models, and in practice requires only once focal calibration for permanent use, achieving a significant reduction in the complexity of focal calibration, and the migrability of the model in scenarios where the camera height changes.

Image processing has led its applications to scale to almost all areas encompassing the emergent interdisciplinary fields of computers, electronics, mechanical, civil, and more. There are several discrete models in image processing to identify characteristic of an object under surveillance. In smart emergency applications, accuracy and precision on attributes of these objects are paramount. Hence there is a need to enhance the image processing algorithms used to measure an object's distance, size, and color from any altitude. The paper demonstrates Hybrid Image Processing Model (HIPM) using Triangle similarity, Pixel Per Metric (PPM), CIELAB color space, and Douglas-Peucker algorithm to compute the distance of an object from the camera, the size, color, and shape of an object from the image, respectively. This work emphasises on leveraging image processing techniques for assisting emergency aircraft landing. Results were obtained with five real-time image sets, each consisting of 50 images, and proved HIPM is efficient and reliable, with an accuracy of 99.84% and a mean error rate of 0.08. This work also discusses the model's capability to function in accordance with the need of autonomous vehicles and military events. [ABSTRACT FROM AUTHOR]

Aiming at the problems of high system cost, complex ranging process, low ranging accuracy, and limited measurement distance in commonly used methods for ground fixed point distance measurement journals, a variable frequency microwave ranging system based on a dual transponder architecture is proposed in this letter. The system uses carrier dual transponder as an active forwarding architecture, utilizing the digital phase‐locked loop to obtain the high‐precision phase difference between two signals, and resolving the ambiguity through microwave frequency sweep within a certain range. The simulation results show that the system can quickly obtain the unambiguous distance between two fixed points, besides, driven by a high‐stability crystal oscillator, submillimetre accuracy can be achieved in km‐level ranging. [ABSTRACT FROM AUTHOR]

Hyperspectral imaging (HSI) is the procedure of acquiring a scene over a wide range of an electromagnetic spectrum for the purpose of detailed analysis and prediction. The occurrence of noise during the acquisition procedure, however, poses a limitation on this imaging system. Noise in HSI is classified as a mixture of Gaussian and impulse noise statistics, and noise removal or denoising forms an integral part of this imaging system. In this letter, we consider the problem of removing this mixed Gaussian–impulse noise from HSI datasets by formulating a joint optimization problem based on the maximum a posteriori (MAP) estimates for Gaussian and impulse noise distributions. The proposed method is then solved using an efficient minimization strategy realized through half-quadratic split. Extensive experimentation on synthetic and real HSI datasets corroborates the effectiveness of the proposed denoising technique. [ABSTRACT FROM AUTHOR]

The violent stellar explosions known as supernovae have received especially strong attention in both the research community and the general public recently. With the advent of space telescopes, the study of these extraordinary events has switched gears and it has become one of the leading fields in modern astrophysics. In this paper, we review some of the recent developments, focusing mainly on studies related to space-based observations. [ABSTRACT FROM AUTHOR]

Real-time detection of the distance between trains is an important way to ensure the safety of rail transit and improve train capacity. In particular, the distance measurement between trains on curved rails is difficult to distance measurement. In order to calculate and optimize the real-time distance measurement formula of the train at the curve, a method of distance measurement of the train at the curve based on machine vision is proposed. In this method, monocular camera is used for track image acquisition, OpenCV algorithm is used to process and analyze the acquired images, and neural network method is used to fit the track line of curved rail, realizing the dynamic extraction of track feature points. The distance formula between trains can be calculated and optimized by building a mathematical model. The experimental results show that the overall error rate of the testing system is less than 9.11% when the duration interval is 40~50 ms. The method for the spacing measurement can be integrated together with other information in the collected images for the information fusion and referred as an important condition in the rail system for a train running. [ABSTRACT FROM AUTHOR]

The safety of cyclists, e-scooters, and micromobility devices in urban environments remains a critical concern in sustainable urban planning. A primary factor affecting this safety is the lateral passing distance (LPD) or dynamic proximity of motor vehicles overtaking micromobility riders. Minimum passing distance laws, where motorists are required to maintain a minimum distance of 1.5 m when passing a cyclist, are difficult to enforce due to the difficulty in determining the exact distance between a moving vehicle and a cyclist. Existing systems reported in the literature are invariably used for research and require manual intervention to record passing vehicles. Further, due to the dynamic and noisy environment on the road, the collected data also need to be manually post-processed to remove errors and false positives, thus making such systems impractical for use by cyclists. This study aims to address these two concerns by providing an automated and robust framework, integrating a low-cost, small single-board computer with a range sensor and a camera, to measure and analyze vehicle–cyclist passing distance and speed. Preliminary deployments in Singapore have demonstrated the system’s efficacy in capturing high-resolution data under varied traffic conditions. Our setup, using a Raspberry Pi 4, LiDAR distance sensor, a small camera, and an automated data clustering technique, had a high success rate for correctly identifying the number of close vehicle passes for distances between 1 and 1.5 m. The insights garnered from this integrated setup promise not only a deeper understanding of interactions between motor vehicles and micromobility devices, but also a roadmap for data-driven urban safety interventions.

Stones are one of the primary objects that impede the normal activity of underground pipelines. As human intervention is difficult inside a narrow underground pipe, a robot with a machine vision system is required. In order to remove the stones during regular robotic inspections, precise stone detection, segmentation, and measurement of their distance from the robot are needed. We applied Mask R-CNN to perform an instant segmentation of stones. The distance between the robot and the segmented stones was calculated using spatial information obtained from a lidar camera. Artificial light was used for both image acquisition and testing, as natural light is not available inside the underground pipe. ResNet101 was chosen as the foundation of the Mask R-CNN, and transfer learning was utilized to shorten the training time. The experimental results of our model showed that the average detection precision rate reached 92.0; the recall rate was 90.0%; and the F1 score rate reached 91.0%. The distance values were calculated efficiently with an error margin of 11.36 mm. Moreover, the Mask R-CNN-based stone detection model can detect asymmetrically shaped stones in complex background and lighting conditions.

Depth maps are single image metrics that carry the information of a scene in three-dimensional axes. Accurate depth maps can recreate the 3D structure of a scene, which helps in understanding the full geometry of the objects within the scene. Depth maps can be generated from a single image or multiple images. Single-image depth mapping is also known as monocular depth mapping. Depth maps are ill-posed problems that are complex and require extensive calibration. Therefore, recent methods use deep learning to develop depth maps. We propose a new method in monocular depth estimation to develop a high-quality depth map. Our approach is based on a convolutional neural network in which we used Res-UNet with a spatial attention model to develop depth maps. The addition of an attention mechanism increases the capability of feature extraction and enhances the boundaries features. It does not add any extra parameters to the network. With our proposed model, we demonstrate that a simple CNN model aided with an attention mechanism can create high-quality depth maps with a small iteration and training time. Our model performs very well compared to the existing state-of-the-art methods on the benchmark NYU-depth v2 dataset. Our model is flexible and can be applied to any depth mapping or multi-segmentation tasks. [ABSTRACT FROM AUTHOR]

In a preceding paper, we have showed as swarm robotics displacement can be related to the deformation of a continuum material, discretized by a lattice network representing the swarm. To reach this aim, it is fundamental to know the swarm configuration, i.e., its shape; this can be computed from the knowledge of the relative distances between its elements and it is studied as a geometry distances problem. Typically, ultrasonic devices are employed to measure the distances. We propose a method based on light signal exchanged between the machines and the computing of the unknown water adsorption coefficient and distance. Aim of this paper is, therefore, to measure distances between underwater elements of the swarm using cheap power LEDs as light source and photodiode as receiver. The receiving photodiode produces a current we can correlate with distance and water adsorption coefficient; we can be able to estimate the two unknown parameters by moving the robots and stressing the emission conditions of the LED diode. Actual work is based on a previous paper where we stressed work conditions of a power LED in shallow water to change its emission characteristics; now, using these results, we can now perform a set of measurements leading to the knowledge of distances d and adsorption coefficient a (λ). The method we propose here can be a possible support to traditional ultrasonic devices [ABSTRACT FROM AUTHOR]

There is a growing consensus in the global health community that the use of communication technologies will be an essential factor in ensuring universal health coverage of the world's population. New technologies can only be used profitably if their accuracy is sufficient. Therefore, we explore the feasibility of using Apple's ARKit technology to accurately measure the distance from the user's eye to their smartphone screen. We developed an iOS application for measuring eyes-to-phone distances in various angles, using the built-in front-facing-camera and TrueDepth sensor. The actual position of the phone is precisely controlled and recorded, by fixing the head position and placing the phone in a robotic arm. Our results indicate that ARKit is capable of producing accurate measurements, with overall errors ranging between 0.88% and 9.07% from the actual distance, across various head positions. The accuracy of ARKit may be impacted by several factors such as head size, position, device model, and temperature. Our findings suggest that ARKit is a useful tool in the development of applications aimed at preventing eye damage caused by smartphone use. [ABSTRACT FROM AUTHOR]

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Emotional expressions form a key part of user behavior on today's digital platforms. While multimodal emotion recognition techniques are gaining research attention, there is a lack of deeper understanding on how visual and non-visual features can be used to better recognize emotions in certain contexts, but not others. This study analyzes the interplay between the effects of multimodal emotion features derived from facial expressions, tone and text in conjunction with two key contextual factors: i) gender of the speaker, and ii) duration of the emotional episode. Using a large public dataset of 2,176 manually annotated YouTube videos, we found that while multimodal features consistently outperformed bimodal and unimodal features, their performance varied significantly across different emotions, gender and duration contexts. Multimodal features performed particularly better for male speakers in recognizing most emotions. Furthermore, multimodal features performed particularly better for shorter than for longer videos in recognizing neutral and happiness, but not sadness and anger. These findings offer new insights towards the development of more context-aware emotion recognition and empathetic systems. [ABSTRACT FROM AUTHOR]

Edge audio devices can reduce data bandwidth requirements by pre-processing input speech on the device before transmission to the cloud. As edge devices are required to ensure always-on operation, their stringent power constraints pose several design challenges and force IC designers to look for solutions that use low standby power. One promising bio-inspired approach is to combine the continuous-time analog filter channels with a small memory footprint deep neural network that is trained on edge tasks such as keyword spotting, thereby allowing all blocks to be embedded in an IC. This paper reviews the historical background of the continuous-time analog filter circuits that have been used as feature extractors for current edge audio devices. Starting from the interpretation of a basic biquad filter as a two-integrator-loop topology, we introduce the progression in the design of secondorder low-pass and band-pass filters ranging from OTA-based to source-follower-based architectures. We also derive and analyze the small-signal transfer function and discuss their usage in edge audio applications. [ABSTRACT FROM AUTHOR]

We propose an omnidirectional measurement method without blind spots by using a convex mirror, which in principle does not cause chromatic aberration, and by using vertical disparity by installing cameras at the top and bottom of the image. In recent years, there has been significant research in the fields of autonomous cars and robots. In these fields, three-dimensional measurements of the surrounding environment have become indispensable. Depth sensing with cameras is one of the most important sensors for recognizing the surrounding environment. Previous studies have attempted to measure a wide range of areas using fisheye and full spherical panoramic cameras. However, these approaches have limitations such as blind spots and the need for multiple cameras to measure all directions. Therefore, this paper describes a stereo camera system that uses a device capable of taking an omnidirectional image with a single shot, enabling omnidirectional measurement with only two cameras. This achievement was challenging to attain with conventional stereo cameras. The results of experiments confirmed an improvement in accuracy of up to 37.4% compared to previous studies. In addition, the system succeeded in generating depth image that can recognize distances in all directions in a single frame, demonstrating the possibility of omnidirectional measurement with two cameras. [ABSTRACT FROM AUTHOR]

Currently, image sensors (cameras), are widely used for image analysis (classification, segmentation, etc.) and synthesis (object detection, tracking, distance detection, etc.). The aim of the study is to lay the theoretical foundations for the development of an image meter based measurement device (image meter) that can be used for laser-meter, lidar-meter, radar and similar industrial purposes. For this purpose, distance detection methods based on image processing in the literature have been studied. The main factors that negatively affect the performance of these methods have been identified, and a new method has been developed that is not affected by these factors. The measuring device, which is planned to be developed, is based on theoretical foundations. The operation of these theoretical foundations is based on hardware and software components. In the study, these theoretical foundations were given and designs of hardware and software components were realized. In study, as a result of calculations made for 1-1000m, it was determined that a success rate below 0.2% could be achieved. The study consists of an electro-mechanical component have hardware and software projects. It is obvious that errors caused by hardware and software components will reduce the success rate. These errors will consist of standard and random errors. In the study, these errors are foreseen and added to the distance measurement equation. Detection of predicted errors will be determined in the future study with the development of the hardware prototype and software components. [ABSTRACT FROM AUTHOR]

The traditional K-means algorithm is very sensitive to the selection of clustering centers and the calculation of distances, so the algorithm easily converges to a locally optimal solution. In addition, the traditional algorithm has slow convergence speed and low clustering accuracy, as well as memory bottleneck problems when processing massive data. Therefore, an improved K-means algorithm is proposed in this paper. In this algorithm, the selection of the initial points in the traditional clustering algorithm is improved first, and then a new global measure, the effective distance measure, is proposed. Its main idea is to calculate the effective distance between two data samples by sparse reconstruction. Finally, on the basis of the MapReduce framework, the efficiency of the algorithm is further improved by adjusting the Hadoop cluster. Based on the real customer data from the JD Mall dataset, this paper introduces the DBI, Rand and other indicators to evaluate the clustering effects of various algorithms. The results show that the proposed algorithm not only has good convergence and accuracy but also achieves better performances than those of other compared algorithms. [ABSTRACT FROM AUTHOR]

Objective. The aim of the study is to build emulators of the main sensors of a small spacecraft (SC).Method. The quality of recovery is determined by the standard deviation (RMS) of the data from the standards. The study is based on the application of the dynamic programming method.Result. A review of works in the field of application of stable characteristics - invariants, in the applied areas of pattern recognition and state control of complex technical systems is carried out. A method aimed at identifying failures and building small spacecraft sensor emulators based on the analysis of stable indicators: correlation and covariance coefficients is proposed and tested. The proposed approach makes it possible to increase the overall reliability of the information-measuring system of a small spacecraft (SC). In the future, to emulate a failed sensor, it is planned to use multiple correlation of time sequences.Conclusion. In the general case, recognition is reduced to comparing the graph structures of objects to the correct matching of hierarchical image models based on various invariants. Control of the states of complex systems is based on the use of stable characteristics that act as invariant or subinvariant descriptors. Invariants in various applications help to recognize the current situation with a sufficient degree of accuracy, usually reducing the dimension of the problem.

The revised Reinforcement Sensitivity Theory (rRST) is an influential personality theory that provides explanations for differences in approach and avoidance tendencies. In this study, we aim to test assumptions of the rRST, originally developed using animal models, in an experimental setting with humans. Therefore, we designed a paradigm in which N = 65 healthy participants are confronted with positive and negative images to measure their unconscious change in distance to the computer screen by means of a face recognition software. Persons with a more sensitive fight-flight-freezing system (FFFS; constituting fear, regulating avoidance) showed stronger distancing from negative images relative to positive images than people with a less sensitive FFFS. The behavioral inhibition system (BIS; constituting anxiety) showed no moderation on distance change, indicating that the systems underlying fear and anxiety adopt different regulatory functions. Concerning the behavioral approach system (BAS; regulating approach), also no effects were observed, although a BAS-dependent approach to positive stimuli was hypothesized. However, there was a main effect of picture valence, indicating closer distances for more positive stimuli. Overall, our data provide further evidence for the validity of the rRST, using the change in distance to the monitor as exciting operationalization of approach and avoidance behavior. • Validation study of the revised Reinforcement Sensitivity (rRST) in humans • The fear system regulates avoidance regarding negative stimuli. • Results provide further evidence for the differentiation between fear and anxiety. • Highly ecological valid paradigm to test individual differences in rRST-traits [ABSTRACT FROM AUTHOR]

This research tackles calibrating surround view camera systems for automated driving, where traditional methods require extensive manual measurements and precise conditions. We introduce a novel automatic calibration method that ensures portability and accuracy using only custom-designed calibration boards placed in the cameras' common view. To address visual interference from extraneous objects, we design a unique board with AprilTag 2D codes, offering clear advantages over traditional checkerboards. Additionally, we have developed an Adaptive Angle Projection to counteract fisheye lens distortions. We also introduce a hierarchical pose optimization strategy that transitions from local to global, incorporating a "Planar-Eccentric constraint" to leverage the calibration board's geometric positioning. Our method achieved 4.01 cm measurement error, with 0.075°angle error and 1.18 cm translation error. Our method's efficacy and robustness were confirmed through extensive testing on an experimental vehicle and simulation dataset, proving its superiority in achieving precise calibration and measurement with minimal manual input. • Introduces a novel calibration method for surround-view systems. • Enhances accuracy with adaptive angle projection and Planar-Eccentric Constraint. • Demonstrates significant advancements in vision measurement for ADAS. • Proves robust against wide-angle distortions and operational challenges. [ABSTRACT FROM AUTHOR]

Distance measurement is an important part of the wireless location technology.The ranging accuracy of millimeter wave based distance measurement depends on the frequency estimation accuracy of beat signals from the targets, but the existing frequency estimation algorithms have the shortcomings of either high complexity or low accuracy.In order to improve the ranging accuracy, the frequency compensation value and frequency estimation method based on all phase FFT amplitude was proposed to improve the performance of frequency estimation.Firstly, the signal amplitude was estimated based on the relationship between its all phase FFT and its conventional FFT spectrum.Moreover, using the relationship between the estimated signal amplitude, the signal spectrum amplitude and frequency compensation value, the frequency compensation value was solved by Newton iterative algorithm.Then, the frequency compensation value was used to shift the frequency of the signal, and the frequency compensation value was estimated again to obtain the final estimation value of the signal frequency, from which the distance of the target was calculated.Finally, simulation results were presented to show that the proposed method has higher distance measurement accuracy than the existing methods.

Different temperature sensors show different measurement values when excited by the same dynamic temperature source. Therefore, a method is needed to determine the difference between dynamic temperature measurements. This paper proposes a novelty approach to treating dynamic temperature measurements over a period of time as a temperature time series, and derives the formula for the distance between the measurement values using uniformsampling within the time series analysis. The similarity is defined in terms of distance to measure the difference. The distance measures were studied on the analog measurement datasets. The results show that the discrete Fréchet distance has stronger robustness and higher sensitivity. The two methods have also been applied to an experimental dataset. The experimental results also confirm that the discrete Fréchet distance performs better.

ToF (Time of Flight) sensors have been gaining popularity in recent times as a cheap and accurate way to take distance measurements. They may prove to be a key component of obstacle detection systems in the near future. This paper presents a stepper motor system with two measurement modules containing ToF sensors. The results of distance measurements made with these modules relative to a flat surface for different sensor modes are presented. Standard deviations were determined for the measurement results and a polynomial fitting was performed using the linear least squares method. The results were used to select the sensor for further work in terms of its use in an obstacle detection system.

Abstract Distance estimation can be achieved by using active sensors or with the help of passive sensors such as cameras. The stereo vision system is generally composed of two cameras to mimic the human binocular vision. In this paper, a Python‐based algorithm is proposed to find the parameters of each camera, rectify the images, create the disparity maps and finally use these maps for distance measurements. Experiments using real‐time images, which were captured from our stereo vision system, of different obstacles positioned at multiple distances (60–200 cm) prove the effectiveness of the proposed program and show that the calculated distance to the obstacle is relatively accurate. The accuracy of distance measurement is up to 99.83%. The processing time needed to calculate the distance between the obstacle and the cameras is less than 0.355 s.

Abstract Background The purpose of this study was to evaluate the accuracy of intraoral scanners by comparing the trueness and precision of several types of scanners in measuring the distance between the ball abutments on pairs of multiple implants. Methods Seven implants were placed on a fully edentulous upper jaw model. After ball abutments were attached to the implants on the master model, the three-dimensional (3D) shape of the model was evaluated using a computer numerical control 3D coordinate-measuring machine. Subsequently, the 3D shape-related data of the model were obtained using two types of intraoral scanners (3M True Definition Scanner [TDS] and 3Shape Trios3 [TR3]) and two types of laboratory scanners (KaVo ARCTICA Auto Scan [KA] and Identica Hybrid [IH]). Using the obtained 3D shape-related data, the trueness and precision in measuring the distance between the balls within seven pairs of ball abutments were compared among the scanners using 3D analysis software. Results Intraoral scanners produced significantly greater errors in trueness and precision than laboratory scanners in measuring the distances between the ball abutments in all the dental regions. Between the intraoral scanners, powder-requiring TDS produced significantly lower errors at inflection points than powder-free TR3. Conclusions These results indicate that an optical impression technique using an intraoral scanner is suitable for dental implant treatment in patients with a few missing teeth.

Topography is the study of an area on the earth's surface. This term relates to the land's slope or contour, which is the interval of elevation differences between two adjacent and parallel contour lines. Topography generally presents a three-dimensional model of object surface relief and an identification of land or hilly areas based on horizontal coordinates such as latitude and longitude, and vertical position, namely elevation. The topography is essential information that must be provided in the execution of building or road construction based on the ground contour. The problem which is the ground contour which can provide visualization topography as a three-dimensional (3D) condition of the ground contour is not normal (non-linear). Another problem is that the traditional measurement techniques with wheel rotation only measure distances and cannot represent the trajectory of the ground contour in 3D. The proposed in-depth evaluation of orientation estimation results in the topography accuracy level. This methodology consists of several processes; Inertia and orientation of an object, Distance measurement, Terrestrial topocentric – Euclidean transformation, and Topography visualization. This research designed a prototype and proposed a new visualization method of the ground contours to reconstruct a topography map between three algorithms; Direct Cosine Matrix-3D Coordinate, Madgwick-3D Coordinate, and Complementary Filter. The methodology was tested and evaluated intensively by direct observation at three measurement locations with different difficulty levels. As a result, the Direct Cosine Matrix-3D Coordinate is able to visualize the ground contours by reconstructing a topography map much better than other methods.

This paper deals with the determination of the absolute errors of a small time of flight (ToF) distance sensor with respect to coloured surfaces at different illumination intensities. The aim was to determine the absolute error of the measured distance by the VL53L1X sensor when set to short-mode and long-mode at different illumination intensities: 10 lx and 350 lx depending on the coloured surface using regression analysis methods. The research was performed using 7 colour samples with different spectral colours determined according to the CIE Lab colour model. Based on the performed experiments, it was found that the error at different sensor settings, change of colour surface and different illumination intensity is approximated by a linear function only up to a certain measured distance. The process is influenced by proposed factors such as: illumination intensity, coloured surface with different illumination reflectance and signal-noise parameters of the tested sensor during the experiment.

Traveling is one of the most popular hobbies nowadays. Natural tourist destinations located in the countryside are a choice for travelers. There are several tourist sites located in the country of Bogor, one of which is in the district of Nanggung. Nanggung sub-district is in the region of west Bogor, approximately 60 km from the capital city of Bogor Regency (Cibinong). The location of the Nanggung sub-district has an area of 135.25 km2, located near the foot of Mount Salak, creating a beautiful atmosphere and quite cold air. There are approximately 30 tourist destinations in the Nanggung sub-district. Base mobile application to help tourists determine the nearest destination. This mobile application helps domestic and foreign tourists select the closest tourist sites visited in Nanggung, Bogor Regency. The haversine method used to calculate the distance is the haversine method. The haversine method estimates the distance. Haversine will calculate the distance between the application user and the location of the surrounding tourist points located in the Nanggung sub-district. This study aims to calculate the length of application users to tourist sites. So that users get convenience in determining the closest location from the application user's position.

Graph learning is a popular approach for perfor ming machine learning on graph-structured data. It has revolutionized the machine learning ability to model graph data to address downstream tasks. Its application is wide due to the availability of graph data ranging from all types of networks to information systems. Most graph learning methods assume that the graph is static and its complete structure is known during training. This limits their applicability since they cannot be applied to problems where the underlying graph grows over time and/or new tasks emerge incrementally. Such applications require a lifelong learning approach that can learn the graph continuously and accommodate new information whilst retaining previously learned knowledge. Lifelong learning methods that enable continuous learning in regular domains like images and text cannot be directly applied to continuously evolving graph data, due to its irregular structure. As a result, graph lifelong learning is gaining attention from the research community. This survey paper provides a comprehensive overview of recent advancements in graph lifelong learning, including the categorization of existing methods, and the discussions of potential applications and open research problems. [ABSTRACT FROM AUTHOR]

Class imbalance data sets are common in a vast variety of real-world application areas. Synthetic minority oversampling technique (SMOTE) is an important technique for processing imbalanced data sets. SMOTE requires the user to preset the number of nearest neighbor instances before synthesizing instances, which is often difficult to choose accurately. Moreover, SMOTE is easy to synthesize minority instances in the majority areas, which leads to the performance degradation of the classifier. To address these issues, in this paper, a novel distance-based arranging oversampling (DAO) technique is proposed. DAO can effectively prevent users from selecting inaccurate hyperparameters, and DAO can be used as an alternative algorithm to replace the SMOTE-based oversampling technique. We further filter the synthesized instances by setting appropriate conditions to avoid generating minority instances in the majority domain. In our experiments, we collect 25 public benchmark data sets from the KEEL database and HDDT database, and apply CART and ID3 classification models on the oversampling training set of each data set to assess our DAO technique. Under the two evaluation metrics, F-measure and kappa, compared with the state-of-the-art oversampling techniques, our proposed method is superior or partially superior to them. [ABSTRACT FROM AUTHOR]

To locate the ground encroachments in transmission line corridor effectively, the application of a novel binocular stereo vision (BSV) for distance measurement is realized with improvement and adjustment in calibration and reconstruction process of BSV. Firstly, a calibration method using a 1‐D pole with spatial constraints to replace the 2‐D calibration object is proposed. The field experiment shows that this method improves the accuracy of the calibration in a large‐scale scene by expanding the calibration area. Then, a regulation algorithm to eliminate the difference between focal lengths for binocular cameras is designed to further improve accuracy of calibration. For practical utilization of BSV in the monitor system, the 3‐D reconstructions from world coordinate system (WCS) to ground‐based system is achieved by rotation and transformation of the reconstructed coordinate system and direct height measurement is realized consequently. Further, based on the calibration method being proposed, the partial calibration with a reduced calibration distribution area is studied considering the probable complex terrain. Height measurement calculations are carried out on both hypothetical and real obstacles to verify the validity of the calibration method and postprocessing algorithms. The results demonstrated that the calibration method algorithms successfully realized the practical application of BSV in large‐scale scene with high accuracy. By using this novel BSV technology, the total transmission line inspection cost is expected to be greatly reduced by comparing with manned helicopter and UAV inspections. [ABSTRACT FROM AUTHOR]

Time-of-flight-based localization requires high-accurate synchronization among the nodes in a network. Gradient clock synchronization (GCS) is a class of distributed algorithms capable of providing the demanded accuracy. Global clock rates defined by GCS algorithms are susceptible to drift relative to the individual hardware clock rates. This is due to the lack of a hard tie to a physical clock rate and is identified as the chaotic clock rate phenomenon. This scales range of measurements and can lead to stability issues in the network. This article presents a novel GCS algorithm for ultrawideband (UWB) ranging networks, addressing the chaotic global clock rate phenomenon. A correction term is introduced in the generic GCS algorithm so that the global clock rate is guaranteed to be converging into the average of individual clock rates. This also achieves asymptotic stability in the clock rate error state. The stability of the generic GCS and the proposed method for time-invariant hardware clock rates are compared using eigenvalue analysis in the clock error state space. A Kalman filter-based technique is used to precisely estimate the interanchor clock dynamics and ranges, which are then used in the asymptotic GCS (AGCS) update rule to calculate synchronization parameters. Simulations and experiments are conducted to evaluate the stability and synchronization accuracy of the proposed algorithm. The localization accuracy is evaluated for an indoor quadcopter localization task, which uses a range-assisted inertial navigation system (INS), resulting in rms position errors in the order of 0.2 m. [ABSTRACT FROM AUTHOR]

Researchers have explored multiple Wi-Fi features to estimate user locations in indoor environments in the past decade, such as Received Signal Strength Indication (RSSI), Channel State Information (CSI), Time of Arrival (TOA), and Angle of Arrive (AoA). Fine Time Measurement (FTM) is a protocol standardized by IEEE 802.11-2016, which can estimate the distance between the initiator and the station using Wi-Fi Round-Trip Time (RTT). Promoted by Google, such a protocol has been explored in many mobile localization algorithms, which can provide meter-level positioning accuracy between Wi-Fi RTT-enabled smartphones and access points (APs). However, previous studies have shown that the Wi-Fi RTT measurements are sensitive to environmental changes, which leads to significant errors in the localization algorithms. Such an error usually varies according to different environments and settings. Therefore, this paper investigates the error in Wi-Fi RTT distance measurements by setting multiple experiments with different hardware, motion status, and signal path loss conditions. The experiment results show that four categories of errors are found in RTT distance measurements, including hardware-dependent bias, blocker-dependent bias, fluctuations, and outliers. Comparison and analysis are carried out to illustrate the impact of the different errors on Wi-Fi RTT distance. [ABSTRACT FROM AUTHOR]

This article deals with the regression analysis of the ultrasonic signal amplitude when the character of the reflection surface has been changed from a planar case to a sharp corner case. The experiment was performed at a measurement distance within the interval from 100 mm to 215 mm. A nonlinear correlation between the amplitude of the ultrasound signal and the measured distance was demonstrated. By analyzing the frequency spectra, a poor nonlinear correlation between the maximum frequency component and the distance vector was found for the sharp corner case versus the planar case, which proved similar nonlinear characteristics as the signal amplitude marker. The strong linear correlation in the distance difference vectors in the amplitude analysis of the ultrasound signal confirmed the hypothesis of a direct relationship between the reflection surface geometric characteristic and the polarity of the difference. The ultrasound signal was identified as a 3rd-order dynamic system. The nonlinear correlation of the steady-state values of the modelled transfer functions versus distance likewise shows the characteristic of the polarity difference or character derivative as a quantification marker of the characteristics of the reflection surface from the geometric point of view. [ABSTRACT FROM AUTHOR]

In this study, we propose a novel Wasserstein distributional tracking method that can balance approximation with accuracy in terms of Monte Carlo estimation. To achieve this goal, we present three different systems: sliced Wasserstein-based (SWT), projected Wasserstein-based (PWT), and orthogonal coupled Wasserstein-based (OCWT) visual tracking systems. Sliced Wasserstein-based visual trackers can find accurate target configurations using the optimal transport plan, which minimizes the discrepancy between appearance distributions described by the estimated and ground truth configurations. Because this plan involves a finite number of probability distributions, the computation costs can be considerably reduced. Projected Wasserstein-based and orthogonal coupled Wasserstein-based visual trackers further enhance the accuracy of visual trackers using bijective mapping functions and orthogonal Monte Carlo, respectively. Experimental results demonstrate that our approach can balance computational efficiency with accuracy, and the proposed visual trackers outperform other state-of-the-art visual trackers on several benchmark visual tracking datasets.

This work eases the feasibility of infrared thermometer application and reliability to introduce a novel design with upgraded applications & functions. The custom-designed compact device "Badge" structured comprises the operative methods through the electronic packages of an optimal level. The physical and social distance measured by the ToF (Time of Flight) infrared laser sensor within 1 m from the subject and the measuring equipment (MLX90632 SMD QFN and VL530LX ToF). When the distance is not maintained, or the physical distance condition is not met, the flashing LED, or vibration should trigger an indication (warning for physical distancing and alteration for pyrexia warning, respectively). Statistical analysis and simulation-based studies criticized the accuracy of ±0.5°F and relational model of the independent and dependent variable for this device with significant R2 = 0.99 and P < = 1; values with the lowest accuracy error of ±0.2°F and least residual sum of squares 0.01462 values. The portable, lightweight, and dynamic body temperature monitoring altered the application from static to continuous, complete structural design. This alternative provides the best technique to combine worn (personnel) medical devices with primary healthcare instruments to help body temperature measurements that are not contactable, fast, and accurate. It builds a way of processing through the protocol Covid-19.

Optical rangefinders based on Self-Mixing Interferometry are widely described in literature, but not yet on the market as commercial instruments. The main reason is that it is relatively easy to propose new elaboration techniques and get results in controlled conditions, while it is very difficult to develop a reliable instrument. In this paper, we propose a laser distance sensor with improved reliability, realized through a wavelength modulation at a different frequency, able to decorrelate single measurement errors and obtain improvement by averages. A dedicated software is implemented to automatically calculate the modulation pre-emphasis, needed to linearize the wavelength modulation. Finally, data selection algorithms allow to overcome signal fading problems due to the speckle effect. A prototype demonstrates the approach with about 0.1 mm accuracy up to 2 m of distance at 200 measurements per second. [ABSTRACT FROM AUTHOR]

This work provides a proof-of-concept for a linear-position sensor using an ultrawideband (UWB) frequency-modulated continuous wave (FMCW) radar system operating at 126–182 GHz. It is the first work to show environmental compensated and calibration-free distance measurements with micron accuracy at medium range using millimeter-wave (mmWave) radar technology. We addressed hardware imperfections, parameter estimation, and the free-space path, i.e., refractive index and near-field effects. The proposed signal processing chain is robust to interference and of low computational cost. Experiments reveal a systematic error of $\pm 1~\mu \text{m}$ over 4.8 m (0.8–5.6 m), and a random error at a minimum of 30 nm, providing very high sensitivity. [ABSTRACT FROM AUTHOR]

Graph distance is also named approximate graph isomorphism, error-tolerant graph matching, it is a measure of similarity (or dissimilarity) between two graphs. The graph distance function is an elusive question remaining a pressing problem in a wide range of fields. In general, graphs are usually enriched with node and edge attributes, namely, heterogeneous graphs, our focus is encapsulating node and edge identities in a graph. In addition, an adequate representation of the graph-adjacency matrix can capture the connectivity of the graph structure, while an adjacency matrix bears high-order combinatorial nature. Solving these challenges in the effective analysis of these graphs is of utmost importance. In this paper, structural and semantic features are both preserved by graphs, so the analysis of row sum and eigenspectra for vertex and edge adjacency matrix has captured the affinity interactions within graphs locally and globally. Therefore, we estimate the geometrical and semantic dissimilarities/distances between graphs extensively and systemically. Several illustrative chemical compound cases implement in practical scenarios, thus, the theory could be easily understood. Our method defines a quantitative distance (or dissimilarity) measure for comparing graphs, it has a good measure of expressiveness and a suited polynomial computation complexity, which paves the way for graph analysis. [ABSTRACT FROM AUTHOR]

As an important research topic of machine learning, multiclass classification has wide applications ranging from computer vision to bioinformatics. A variety of multiclass classification algorithms with promising performance have been proposed. Among them, the decomposition-based algorithms have shown their competitiveness, since they transform the original problem into several easily solved binary classification sub-problems. Unlike existing decomposition-based algorithms which tackle each sub-problem independently, this paper suggests an evolutionary multitasking method, named EMT-MC, for multiclass classification, where the concept of multitasking is introduced to achieve the multiclass classifier with better quality. To be specific, in EMT-MC, each binary classification sub-problem is firstly viewed as a task. Then, during the evolution, the tasks with low performance (termed "ill-solved" tasks) are aided by some well-selected "assisting" tasks by using the evolutionary multitasking learning. This not only ensures that the useful information in "assisting" tasks can be transferred into those "ill-solved" tasks, but also helps them to achieve classifiers with higher accuracy. Numerical experiments on different multiclass classification datasets demonstrate the superiority of the proposed method over the state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

Hafnium oxide based ferroelectric (FE) memory concepts like FE field effect transistors (FeFETs) will become increasingly important. They are highly scalable, provide high operation speed, and consume low power. Just recently, an 1T1C FeFET concept with one transistor (1T) and one separated FE capacitor (1C) was demonstrated. This alternative approach is promising to overcome the drawbacks usually observed for the classic 1T concept like limited endurance, reduced retention, and high device-to-device variability. Electrically, the 1T1C FeFET consists of a series connection of the FE capacitor and the gate oxide capacitance. To operate at low voltages, a large fraction of the applied voltage must drop across the FE, which can be achieved by optimizing the capacitance ratio. Herein, 1T1C FeFETs with various capacitance ratios are fabricated and its impact on the electrical performance is discussed. Furthermore, the observed endurance of up to 10 8 field cycles illustrates the great potential of the new concept. [ABSTRACT FROM AUTHOR]

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