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Copyright of Forensic Science & Technology is the property of Institute of Forensic Science, Ministry of Public Security and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

With the advancement of artificial intelligence, visual ranging has become a widely researched field. This paper introduces a novel method for distance measurement by evaluating the sharpness of objects in the current frame. It is well known that the image is sharpest at the camera's focal point and becomes blurry when moving away from it. Leveraging this characteristic, this study utilizes sharpness to achieve distance measurement. Initially, the specific orientation of the target object is identified and located. The image area of the target object is cropped in a certain direction, and its image quality is assessed through sharpness calculation. Subsequently, the relationship between sharpness and distance values is analysed statistically, and a function curve depicting their correlation is plotted. Consequently, the distance between the target object and the detector can be determined based on sharpness with an extremely small margin of error using this method. [ABSTRACT FROM AUTHOR]

The technology of intelligent vehicle perception system based on vision sensor is becoming more and more mature, and many distance measurement methods based on monocular vision have been proposed. However, less attention has been paid to the experiment and application of the distance measurement of the vehicle in front of the campus intelligent patrol vehicle. In this paper, two monocular visual distance measurement methods based on the width of the target detection object and the distance between the target detection object and the ground contact point are proposed. Two distance measurement models are proposed according to the principle of camera imaging model and the principle of coordinate system transformation. Then, two kinds of distance measurement experiments are designed and compared. The experimental results show that the measurement method based on the contact point between the target and the ground is poor, but the absolute error is less than 0.67 m. The overall error of distance measurement method based on license plate width is the smallest, and the absolute error is less than 0.15 m. The distance measurement error of the detection box based on the width of the vehicle body is large, and the absolute error is kept within 0.31 m. The measurement accuracy of the two methods for the distance measurement of the vehicle in front of the campus patrol vehicle meets the detection requirements. This work is significant to the research on the distance measurement of the vehicle in front of campus patrol vehicles and it is of great significance to enhance the safety of the campus patrol vehicle. [ABSTRACT FROM AUTHOR]

Abstract Synthetic images facilitate stereo matching. However, synthetic images may suffer from image distortion, domain bias, and stereo mismatch, which would significantly restrict the widespread use of stereo matching models in the real world. The first goal in this paper is to synthesize real‐looking images for minimizing the domain bias between the synthesized and real images. For this purpose, sharpened disparity maps are produced from a mono real image. Then, stereo image pairs are synthesized using these imperfect disparity maps and the single real image in the proposed pipeline. Although the synthesized images are as realistic as possible, the domain styles of the synthesized images are always very different from the real images. Thus, the second goal is to enhance the domain generalization ability of the stereo matching network. For that, the feature extraction layer is replaced with a teacher–student model. Then, a constraint of binocular contrast features is imposed on the output of the model. When tested on the KITTI, ETH3D, and Middlebury datasets, the accuracy of the method outperforms traditional methods by at least 30%. Experiments demonstrate that the approaches are general and can be conveniently embedded into existing stereo networks.

Synthetic images facilitate stereo matching. However, synthetic images may suffer from image distortion, domain bias, and stereo mismatch, which would significantly restrict the widespread use of stereo matching models in the real world. The first goal in this paper is to synthesize real‐looking images for minimizing the domain bias between the synthesized and real images. For this purpose, sharpened disparity maps are produced from a mono real image. Then, stereo image pairs are synthesized using these imperfect disparity maps and the single real image in the proposed pipeline. Although the synthesized images are as realistic as possible, the domain styles of the synthesized images are always very different from the real images. Thus, the second goal is to enhance the domain generalization ability of the stereo matching network. For that, the feature extraction layer is replaced with a teacher–student model. Then, a constraint of binocular contrast features is imposed on the output of the model. When tested on the KITTI, ETH3D, and Middlebury datasets, the accuracy of the method outperforms traditional methods by at least 30%. Experiments demonstrate that the approaches are general and can be conveniently embedded into existing stereo networks. [ABSTRACT FROM AUTHOR]

In the present paper, an affordable innovative physical experimental equipment consisting of an upper computer, an ultrasonic sensor module, and an Arduino microcontroller has been designed. The relationship between the position of the slider fixed on two springs and time is measured by using the ultrasonic sensor module. A system for slider motion data and image acquisition is constructed by using the LabVIEW interface of Arduino UNO R3. The purpose of this experiment is to demonstrate and interpret the propagation of waves represented by harmonic motion. The spring oscillator system including a slider and two springs is measured and recorded, and the motion can be realized using curve fitting to the wave equation in Sigmaplot. The vibration periods obtained from experimental measurements and curve fitting of the wave equation are 1.130 s and 1.165 s, respectively. The experimental data are in good agreement with the theoretical model. The experimental measurement results show that the maximum kinetic energy is 0.0792 J, the maximum potential energy is 0.0795 J, and the total energy at the position of half the amplitude is 0.0791 J. The results verify the mechanical energy conservation of spring oscillator system in a short time. This self-made instrument has improved the visualization and the automation level of the corresponding experiments. [ABSTRACT FROM AUTHOR]

Most existing road infrastructures were constructed before the emergence of automated vehicles (AVs) without considering their operational needs. Whether and how AVs could safely adapt to as-built highway geometry are questions that remain inconclusive, and a plausible concern is a challenge from vertical alignments. To fill this gap, this study uses a virtual simulation to investigate the available sight distance (ASD) of AVs on vertical alignments subject to the current highway geometric design specification and its implications for speed limits. According to the scenario generation framework, several scenarios featuring vertical geometric elements and lidar sensors were created and tested. Moreover, the maximum speed for adequate ASD is calculated to determine the AV speed limit, considering safe sight distance and speed consistency requirements. The results indicate that crest curves are not disadvantaged in ASD compared with either sag curves or tangent grades. Only equipped with multichannel lidar and advanced perception algorithms enabling a lower detection threshold would a level 4 AV be compatible with the as-built vertical alignment with a design speed (Vd) of 100 km/h. However, a level 3 AV can only adapt to the vertical profile with Vd = 60 km/h. The findings of this study should be of interest to the road-oriented operational design domain and support road administrators in regulating AV safe speeds. [ABSTRACT FROM AUTHOR]

A method is proposed for measuring absolute distances from the low-frequency spectrum of the interference signal of a frequency-modulated laser diode. The method of modulation of the self-mixing laser signal according to the harmonic law is used, in the spectrum of which a wide range of frequency components is observed. The connection between the maximum frequency of change of the interference signal and the absolute distance to the reflector is shown. A linear dependence of the frequency corresponding to the inflection region of the decay envelope of the interference signal spectrum on the distance is shown, which can be used to implement a non-contact distance measurement method with harmonic modulation of the self-mixing laser supply current. [ABSTRACT FROM AUTHOR]

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. [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. [ABSTRACT FROM AUTHOR]

To attain a direct MIDI output from an electric guitar, we devised and implemented a sophisticated laser sensor system capable of measuring finger positions. This sensor operates on the principle of optical triangulation, employing six lasers and seven position-sensing detectors that are time-multiplexed. The speed and precision of this sensor system meet the necessary criteria for creating an electric guitar with a direct digital output, perfectly satisfying the application's requirements. [ABSTRACT FROM AUTHOR]

Spatial perception plays a critical role in virtual worlds and real environments, as it can impact navigation abilities. To understand this influence, the conducted study investigated the effects of human characteristics and immersion levels on the exocentric distance estimation process in virtual environments. As the first step, a virtual environment was implemented for both desktop and Gear VR head-mounted displays. Afterward, the exocentric distance estimation skills of 229 university students were examined. Out of these students, 157 used the desktop display, and 72 used the Gear VR. Using logistic regression analysis and linear regression analysis methods, their effects on the probabilities of accurate estimates and their estimation times were investigated. According to the results, gender, video game playtime per week, height, and display device had significant effects on the former, whereas dominant hand, video game playtime per week, height, and display device had significant effects on the latter. The results also show that by using the head-mounted display, the likelihood of the students estimating exocentric distances accurately significantly decreased; however, they were significantly faster with it. These findings can influence the development of more accessible and effective virtual environments in the future. [ABSTRACT FROM AUTHOR]

Safety is the foundation of urban sustainable development. The urban construction and operation process involves a large amount of multidimensional time series data. By detecting anomalies in these multidimensional time subsequences (MTSs), decision support can be provided for early warning of urban construction and operation risks. Considering the complexity of urban infrastructure, there is an urgent need for fast and accurate anomaly detection. This paper proposes a real-time anomaly detection algorithm based on improved distance measurement (RADIM). RADIM retains the relationships between dimensions in multidimensional subsequences, using an Extended Frobenius Norm with Local Weights (EFN_lw) and a Euclidean distance based on multidimensional data (ED_mv) to measure the similarity of MTSs. Moreover, a threshold update mechanism based on First-order Mean Difference (TMFD) is designed to detect real-time anomalies by assessing deviations. This method has been applied to tunnel construction. According to comparative experiments, RADIM exhibits better adaptability, real-time performance, and accuracy in risk warning of tunnel boring machines and construction status. [ABSTRACT FROM AUTHOR]

Autonomous agricultural vehicles rely on accurate real-time positioning information to perform precise farming operations, including seeding, fertilizing and harvesting. Although many positioning solutions exist on the market, their cost is out of many farmers' budgets. Relative positioning can be used for accurate positioning purposes in agriculture applications. Despite the fact that several research attempts have addressed the issue of relative positioning, no agriculture-specific study appears to exist in the literature at the time of writing. In this paper, the opportunity of using real-time GPS-based relative positioning for agriculture applications instead of the typical absolute positioning solutions has been investigated. To this end, we propose to implement and evaluate different relative positioning approaches, namely absolute position differencing, pseudorange single differencing as well as pseudorange double differencing (PDD). Real-world experiments, including static and dynamic scenarios, have been conducted using low-cost single-frequency GPS receivers in an open sky environment and an agricultural field. The obtained results show the superiority of the PDD approach with about 76% of estimation errors less than 1 m in the open sky environment and more than 97% of estimation errors less than 3 m in the agricultural field, representing a considerable accuracy enhancement compared to GPS standalone positioning. [ABSTRACT FROM AUTHOR]

The eye tracking‐based autostereoscopic 3D display provides a large viewing angle, low crosstalk, and large depth perception 3D image experience without using any 3D eyeglasses. However, accurate and fast eye position detection and tracking are still challenging. This study presents an accurate and fast eye positioning algorithm by reducing the computational load of stereo matching and stereo rectification in stereo vision. The accuracy of distance measurement is up to 98.46%. The processing time needed to calculate the distance between the target and the cameras is less than 0.012s. [ABSTRACT FROM AUTHOR]

The problem of non-linearly separable data points requires more efforts to classify the data sample with high accuracy. This paper proposes a new classification approach that employs intuitionistic fuzzy sets to accurately classify non-separable datasets and to efficiently deal with uncertain labelled datasets. The dataset used contains 124 students with 9 features and 1 class for each student. First, the dataset is normalized to train and test the proposed approach. Second, the intuitionistic fuzzy sets were constructed using three features and the fuzzy model was created by calculating the equation of the straight line passing through the intuitionistic fuzzy sets of dataset classes. Finally, the classification is performed by calculating the distance between each class and the unseen sample that is subject to classification. Experimental results show that the classification performance of the proposed approach is competitive and superior to that of other state-of-the-art algorithms on the aforementioned dataset. [ABSTRACT FROM AUTHOR]

Solving various applied problems often requires reconstructing a three-dimensional scene and determining distances to objects from images taken by cameras from several shooting points. Determining the shooting parameters and camera characteristics (calibration of the recording system) directly from the received images is an important stage of reconstruction, which determines the reliability of the results obtained. Using cameras with a small field of view leads to significant errors in calibration and reconstruction. The paper discusses a way to increase the accuracy of calibration by expanding the angle of view by "stitching" a series of images obtained from one shooting point into a wide-angle panorama. In addition to objects, the panorama must include other shooting points. An example of estimating distances to objects in a real urban scene is given. It is shown that the proposed method provides accuracy comparable to measuring distances on a large-scale map. [ABSTRACT FROM AUTHOR]

In recent years, with the continuous promotion of the " Ocean Power" strategy, Underwater Wireless Optical Communication (UWOC) has played an important role in marine military, underwater environment monitoring, submarine oil exploration and marine science research due to its advantages in large bandwidth, fast rate, low power consumption and high security. UWOC has become a new feasible underwater communication technology. The positioning technology is now regarded as the basis of UWOC applications and one of the key techniques in underwater target detection & moving object tracking, which has been favored by researchers. In this paper, the development of UWOC and related research results are introduced in detail, with diverse UWOC localization methods being analyzed. Besides, the research status, the challenges and the development prospects for UWOC localization technology are also discussed.

With the advancement of artificial intelligence, visual ranging has become a widely researched field. This paper introduces a novel method for distance measurement by evaluating the sharpness of objects in the current frame. It is well known that the image is sharpest at the camera’s focal point and becomes blurry when moving away from it. Leveraging this characteristic, this study utilizes sharpness to achieve distance measurement. Initially, the specific orientation of the target object is identified and located. The image area of the target object is cropped in a certain direction, and its image quality is assessed through sharpness calculation. Subsequently, the relationship between sharpness and distance values is analysed statistically, and a function curve depicting their correlation is plotted. Consequently, the distance between the target object and the detector can be determined based on sharpness with an extremely small margin of error using this method.

Laser is a very useful technology in the field of welding to obtain the deepest point of the metal being joined by providing a light source at the weld seam. However, laser imaging may be challenging because there are various laser reflection profiles on the workpiece that make it difficult to extract the desired laser image. In this paper, an extension study on feature point extraction was proposed to determine the depth of the V-groove. By taking use of laser image which has intensity noise around the edges, a noise rejection technique is applied to improve the quality of laser image. A data fitting method for the purpose of extracting feature points based on the reference row has been proposed because it is suitable for use after the laser centerline search. Then, the feature points are obtained by assigning the orientation position based on the "V" shape. Afterward, the extension study from feature point extraction looks on how to determine the V-groove depth by using the intersection and distance measurement method. To validate the accuracy of the proposed method, several samples of straight line and half-moon line types were tested. The performance of the system was evaluated against the actual value. It was observed that the proposed method is acceptable when subjected to laser reflection and lighting variations. The proposed method matches accurately when the respective line types showed an error percentage within 2 to 6%. This study is an extension step from feature point extraction, which can provide further analysis for weld seam tracking applications [ABSTRACT FROM AUTHOR]

Long-term daily-life body signal monitoring offers numerous advantages, such as timely response to health alerts, diseases monitoring, and reducing time and expenses related to clinical trials. Access to physiological data can be achieved with low-cost and comfortable wireless wearable sensors. In our previous publication, we reported a low-cost, easy to implement, and unobtrusive wireless resistive analog passive (WRAP) sensor to provide a feasible bio-signal monitoring technique by using a pair of printed spiral coils (PSC) in a near field connection. Sensitivity, defined as the response to the transducer, is a critical feature in the establishment of a reliable system. In the previous publication, we presented the utilization of a Genetic Algorithm to design a pair of coils and related components to maximize sensitivity. Although the coils' misalignment can significantly affect the optimized sensitivity, it was not incorporated into the optimization process. This paper focuses on optimizing the coils and components in order to maximize both their sensitivity and their resilience against movements of the PSC pair. In a square-shaped pair comprising a primary coil of 60 mm and a secondary coil of 20 mm dimensions, we found that the sensitivity is maximized at 1.3 mƱ for a 16 mm axial distance. Additionally, it remains above 0.65 mƱ within ±11.25 mm lateral and +14 mm axial displacements. [ABSTRACT FROM AUTHOR]

The paper comprehensively addresses the integration of advanced technologies to enhance defense capabilities, with a particular focus on critical tasks such as object detection, tracking, and distance measurement. To this end, the integration of IMX219-77 cameras and Nvidia Jetson Nano is proposed, emphasizing the utilization of their respective features. Commonly used tools like open source computer vision (OpenCV) and GStreamer are preferred for ensuring cohesive integration between hardware and software components. On the software front, tools such as OpenCV and GStreamer are preferred for tasks related to computer vision and multimedia processing. The MOSSE algorithm is selected for object tracking due to its speed, efficiency, and resilience to changes in lighting conditions. Additionally, distance measurement is achieved through the use of Stereo Vision techniques. The results of the study demonstrate the effectiveness and accuracy of the proposed integration. It is found that accurate distance measurements with a margin of error ranging from 0 to 2 mm, falling within acceptable limits mentioned in relevant literature, can be achieved. This underscores the efficacy of the proposed technologies for tasks such as object detection, tracking, and distance measurement. The aim of the study is to conduct an in-depth examination of the integration of advanced tools such as IMX219-77 cameras and Nvidia Jetson Nano for use in defense operations. It seeks to showcase how this integration can strengthen defense strategies and provide protection against potential threats. Additionally, the study aims to lay the groundwork for ongoing innovation and development in defense technologies. In conclusion, the integration of electrooptical systems and computer vision technologies has the potential to significantly enhance defense capabilities and contribute to national security efforts. The advantages provided by this integration can serve as a valuable resource for researchers seeking to develop new solutions in defense and security domains. [ABSTRACT FROM AUTHOR]

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 FROM AUTHOR]

This letter studies the impact of the ultra-wideband (UWB) sensor placement for a zero velocity update aided pedestrian inertial navigation solution that uses foot-to-foot ranging feedback to improve its localization accuracy. Several sensor configurations are investigated in both static and dynamic cases. The root mean square error is used as the performance metric for the static measurement accuracy, whereas the circular error probable is chosen to evaluate the sensor configurations in dynamic experiments.

In the present paper, an affordable innovative physical experimental equipment consisting of an upper computer, an ultrasonic sensor module, and an Arduino microcontroller has been designed. The relationship between the position of the slider fixed on two springs and time is measured by using the ultrasonic sensor module. A system for slider motion data and image acquisition is constructed by using the LabVIEW interface of Arduino UNO R3. The purpose of this experiment is to demonstrate and interpret the propagation of waves represented by harmonic motion. The spring oscillator system including a slider and two springs is measured and recorded, and the motion can be realized using curve fitting to the wave equation in Sigmaplot. The vibration periods obtained from experimental measurements and curve fitting of the wave equation are 1.130 s and 1.165 s, respectively. The experimental data are in good agreement with the theoretical model. The experimental measurement results show that the maximum kinetic energy is 0.0792 J, the maximum potential energy is 0.0795 J, and the total energy at the position of half the amplitude is 0.0791 J. The results verify the mechanical energy conservation of spring oscillator system in a short time. This self-made instrument has improved the visualization and the automation level of the corresponding experiments.

A novel method for the generation of an optical frequency comb (OFC) is presented. The proposed approach uses a laser diode with optical feedback and operating at a specific nonlinear dynamic state named periodic window. In this case, the laser spectrum exhibits a feature with a series of discrete, equally spaced frequency components, and the repetition rate can be flexibly adjusted by varying the system parameters (e.g., external cavity length), which can provide many potential applications. As an application example, a dual-OFC system for distance measurement is presented. The results demonstrate the system's ability to achieve target distance detection, underscoring its potential for real-world applications in this field. [ABSTRACT FROM AUTHOR]

Copyright of Study on Optical Communications / Guangtongxin Yanjiu is the property of Study on Optical Communications Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

As our world becomes increasingly interconnected, technology continues to evolve at an unprecedented pace. One of the most intriguing advancements in recent years is the development of emotion recognition technologies. These systems, powered by artificial intelligence algorithms for machine reasoning and learning, running on sophisticated hardware, can analyze human expressions, vocal tones, and body language to identify and interpret emotions. While the potential applications of this technology are vast, ranging from mental health support to enhancing customer experiences , it also raises significant concerns about personal privacy and public safety. This article delves into the complex implications of emotion recognition technologies, exploring the delicate balance between the promise of improved public safety and the protection of individual privacy. [ABSTRACT FROM AUTHOR]

Solid-state NMR (ss-NMR) is a powerful tool to investigate noncrystallizable, poorly soluble molecular systems, such as membrane proteins, amyloids, and cell walls, in environments that closely resemble their physical sites of action. Rotational-echo double resonance (REDOR) is an ss-NMR methodology, which by reintroducing heteronuclear dipolar coupling under magic angle spinning conditions provides intramolecular and intermolecular distance restraints at the atomic level. In addition, REDOR can be exploited as a selection tool to filter spectra based on dipolar couplings. Used extensively as a spectroscopic ruler between isolated spins in site-specifically labeled systems and more recently as a building block in multidimensional ss-NMR pulse sequences allowing the simultaneous measurement of multiple distances, REDOR yields atomic-scale information on the structure and interaction of proteins. By extending REDOR to the determination of 1H–X dipolar couplings in recent years, the limit of measurable distances has reached ~15–20 Å, making it an attractive method of choice for the study of complex biomolecular assemblies. Following a methodological introduction including the most recent implementations, examples are discussed to illustrate the versatility of REDOR in the study of biological systems. [ABSTRACT FROM AUTHOR]

Directed at the problems of low positioning accuracy and irregular section forming of cutting heads of road header in coal mine production sites, a new cutting head positioning system based on ultra-wideband positioning technology is proposed based on the cutting head motion model and the working principle of ultra-wideband positioning technology, which verifies the anti-interference and the accuracy of its positioning. Combined with the simulation experiment under on-site working conditions, the influence degree of three typical influencing factors on positioning accuracy was obtained, and the accuracy optimization of the ultra-wideband positioning system was guided. Through the dynamic solution experiment, the positioning accuracy of the system is measured, and the results are verified based on the positioning system solution accuracy evaluation standard. [ABSTRACT FROM AUTHOR]

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.

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.

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.

To attain a direct MIDI output from an electric guitar, we devised and implemented a sophisticated laser sensor system capable of measuring finger positions. This sensor operates on the principle of optical triangulation, employing six lasers and seven position-sensing detectors that are time-multiplexed. The speed and precision of this sensor system meet the necessary criteria for creating an electric guitar with a direct digital output, perfectly satisfying the application’s requirements.

In this paper, a system for absolute distance measurement is proposed based on laser frequency scanning interferometry (FSI). The system utilizes a digitally tunable laser as the light source and employs synchronized pulses to drive an analog-to-digital converter (ADC) for interference signal acquisition. The frequency domain demodulation for absolute distance measurement is achieved through a three-spectrum line interpolation method based on the Hanning window. The system takes advantage of the spatial filtering characteristics of a single-mode optical fiber and the diffuse reflection properties of light to achieve a high integration of the prism system that forms the interference optical path. The resulting integrated fiber-optic probe is capable of measuring the distance to a non-cooperative target even when oriented at a certain angle with the target. We designed and fabricated a portable prototype. Experimental validation demonstrated that the maximum measurement distance of the system is 73.51 mm with a standard deviation of less than 0.19 μm for optimal measurement results. Even when there is an offset angle, the system maintains good measurement repeatability.

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]

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.

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.