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This paper proposes a registration approach rooted in point cloud clustering and segmentation, named Clustering and Segmentation Normal Distribution Transform (CSNDT), with the aim of improving the scope and efficiency of point cloud registration. Traditional Normal Distribution Transform (NDT) algorithms face challenges during their initialization phase, leading to the loss of local feature information and erroneous mapping. To address these limitations, this paper proposes a method of adaptive cell partitioning. Firstly, a judgment mechanism is incorporated into the DBSCAN algorithm. This mechanism is based on the standard deviation and correlation coefficient of point cloud clusters. It improves the algorithm's adaptive clustering capabilities. Secondly, the point cloud is partitioned into straight-line point cloud clusters, with each cluster generating adaptive grid cells. These adaptive cells extend the range of point cloud registration. This boosts the algorithm's robustness and provides an initial value for subsequent optimization. Lastly, cell segmentation is performed, where the number of segments is determined by the lengths of the adaptively generated cells, thereby improving registration accuracy. The proposed CSNDT algorithm demonstrates superior robustness, precision, and matching efficiency compared to classical point cloud registration methods such as the Iterative Closest Point (ICP) algorithm and the NDT algorithm. [ABSTRACT FROM AUTHOR]

Bioradar systems, in general, refer to radar systems used for the detection of vital signs. These systems hold significant importance across various sectors, particularly in healthcare and surveillance, due to their capacity to provide contactless solutions for monitoring physiological functions. In these applications, the primary challenge lies in the presence of random body movements (BMs), which can significantly hinder the accurate detection of vital signs. To compensate the affected signal in a timely manner, portions of BM must be correctly identified. To address this challenge, this work proposes a solution based on the Density-Based Spatial Clustering of Applications with Noise (DBScan) algorithm to detect the occurrence of BM in radar signals. The main idea of this algorithm is to cluster the radar samples, aiming to differentiate between segments in which the subject is stable and segments in which the subject is moving. Using a dataset involving eight subjects, the proposed method successfully detects three types of body movements: chest movement, body rotation, and arm movement. The achieved results are promising, with F1 scores of 0.83, 0.73, and 0.8, respectively, for the detection of each specific movement type. [ABSTRACT FROM AUTHOR]

Alpha-synuclein (aSyn) aggregates in the central nervous system are the main pathological hallmark of Parkinson's disease (PD). ASyn aggregates have also been detected in many peripheral tissues, including the skin, thus providing a novel and accessible target tissue for the detection of PD pathology. Still, a wellestablished validated quantitative biomarker for early diagnosis of PD that also allows for tracking of disease progression remains lacking. The main goal of this research was to characterize aSyn aggregates in skin biopsies as a comparative and quantitative measure for PD pathology. Using direct stochastic optical reconstruction microscopy (dSTORM) and computational tools, we imaged total and phosphorylated-aSyn at the single molecule level in sweat glands and nerve bundles of skin biopsies from healthy controls (HCs) and PD patients. We developed a user-friendly analysis platform that offers a comprehensive toolkit for researchers that combines analysis algorithms and applies a series of cluster analysis algorithms (i.e., DBSCAN and FOCAL) onto dSTORM images. Using this platform, we found a significant decrease in the ratio of the numbers of neuronal marker molecules to phosphorylated-aSyn molecules, suggesting the existence of damaged nerve cells in fibers highly enriched with phosphorylatedaSyn molecules. Furthermore, our analysis found a higher number of aSyn aggregates in PD subjects than in HC subjects, with differences in aggregate size, density, and number of molecules per aggregate. On average, aSyn aggregate radii ranged between 40 and 200 nm and presented an average density of 0.001-0.1 molecules/nm2. Our dSTORM analysis thus highlights the potential of our platform for identifying quantitative characteristics of aSyn distribution in skin biopsies not previously described for PD patients while offering valuable insight into PD pathology by elucidating patient aSyn aggregation status. [ABSTRACT FROM AUTHOR]

Autonomous robots operate on batteries, rendering power efficiency essential. The substantial computational demands of object detection present a significant burden to the low-power cores employed in these robots. Therefore, we propose a grid-based density-based spatial clustering of applications with a noise (DBSCAN) clustering accelerator for light detection and ranging (LiDAR)'s point cloud to accelerate computational speed and alleviate the operational burden on low-power cores. The proposed method for DBSCAN clustering leverages the characteristics of LiDAR. LiDAR has fixed positions where light is emitted, and the number of points measured per frame is also fixed. These characteristics make it possible to impose grid-based DBSCAN on clustering a LiDAR's point cloud, mapping the positions and indices where light is emitted to a 2D grid. The designed accelerator with the proposed method lowers the time complexity from O (n 2) to O (n) . The designed accelerator was implemented on a field programmable gate array (FPGA) and verified by comparing clustering results, speeds, and power consumption across various devices. The implemented accelerator speeded up clustering speeds by 9.54 and 51.57 times compared to the i7-12700 and Raspberry Pi 4, respectively, and recorded a 99% reduction in power consumption compared to the Raspberry Pi 4. Comparisons of clustering results also confirmed that the proposed algorithm performed clustering with high visual similarity. Therefore, the proposed accelerator with a low-power core successfully accelerated speed, reduced power consumption, and effectively conducted clustering. [ABSTRACT FROM AUTHOR]

Alpha-synuclein (aSyn) aggregates in the central nervous system are the main pathological hallmark of Parkinson’s disease (PD). ASyn aggregates have also been detected in many peripheral tissues, including the skin, thus providing a novel and accessible target tissue for the detection of PD pathology. Still, a well-established validated quantitative biomarker for early diagnosis of PD that also allows for tracking of disease progression remains lacking. The main goal of this research was to characterize aSyn aggregates in skin biopsies as a comparative and quantitative measure for PD pathology. Using direct stochastic optical reconstruction microscopy (dSTORM) and computational tools, we imaged total and phosphorylated-aSyn at the single molecule level in sweat glands and nerve bundles of skin biopsies from healthy controls (HCs) and PD patients. We developed a user-friendly analysis platform that offers a comprehensive toolkit for researchers that combines analysis algorithms and applies a series of cluster analysis algorithms (i.e., DBSCAN and FOCAL) onto dSTORM images. Using this platform, we found a significant decrease in the ratio of the numbers of neuronal marker molecules to phosphorylated-aSyn molecules, suggesting the existence of damaged nerve cells in fibers highly enriched with phosphorylated-aSyn molecules. Furthermore, our analysis found a higher number of aSyn aggregates in PD subjects than in HC subjects, with differences in aggregate size, density, and number of molecules per aggregate. On average, aSyn aggregate radii ranged between 40 and 200 nm and presented an average density of 0.001–0.1 molecules/nm2. Our dSTORM analysis thus highlights the potential of our platform for identifying quantitative characteristics of aSyn distribution in skin biopsies not previously described for PD patients while offering valuable insight into PD pathology by elucidating patient aSyn aggregation status.

To enhance understanding of congestion points at ports and provide visibility into the incoming goods flow into the USA, this study focuses on maritime ports, using the Port of Boston and New York/New Jersey as case studies. Based on the Automatic Information System (AIS) data, we aim to develop predictive models for port congestion status and the Estimated Time of Arrival (ETA) of container ships. Additionally, we analyze historical commodity flow data to forecast future values, weights, volumes and categories based on Harmonized System (HS) codes. Employing quantitative AIS data analysis provides insights into port congestion dynamics and commodity flow trends, indicating the potential to improve the accuracy of ETA, port management and logistics visibility. This study contributes to both theoretical and practical applications in maritime logistics. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Engineering for Thermal Energy & Power / Reneng Dongli Gongcheng is the property of Journal of Engineering for Thermal Energy & Power 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.)

The interrupted sampling repeater jamming (ISRJ) is a type of coherent jamming that can not only improve the detection threshold of adjacent range cells but also induce the radar system to track a false target. In order to suppress ISRJ, we have developed an interference identification and suppression method based on a clustering algorithm. First, we obtain the time-frequency (TF) domain matrix by applying the short-time Fourier transform (STFT) to the received signal containing the target echo and ISRJ. Then, the time-frequency domain matrix is subjected to threshold decision to obtain a series of over-threshold points which are then processed by the density-based spatial clustering of applications with noise (DBSCAN) algorithm to form a series of clusters. By analyzing the number of points of these clusters, we can identify whether the cluster corresponds to the target echo or ISRJ. Recognition mainly depends on the number of cluster points and the corresponding point threshold allowing for good recognition without the need for accurate estimation of ISRJ parameters. An accurate time-frequency domain filter is constructed based on the recognition results to avoid damaging the spectral structure of the target echo while suppressing the interference. We simulate the method and verify the effect of different combinations of input signal to noise ratio (SNR) and input signal to jamming ratio (SJR) on the performance of the method through Monte Carlo simulation. The simulation results demonstrate that this method has a good suppression effect for the three ISRJ modes under various combinations of SNR and SJR.

Photon-counting light detection and ranging is very sensitive to ambient interference, target features, and instrument performance, especially for long-distance detection of spaceborne laser altimeter and measurement of complex land-cover types with steep terrain. It is crucial to extract the signal photons on the ground surface from the collected photon point cloud (PPC). An adaptive signal photon detection method is presented in this article, which combines histogram statistics and boxplot analysis with density-based spatial clustering of applications with noise (DBSCAN), to denoise the PPC data with strong and weak noise obtained by ice, cloud, and land elevation satellite-2 laser altimeter. First, a coarse denoising with histogram of elevation is conducted on the raw PPC to reduce the calculation amount. Second, a fine denoising based on adaptive DBSCAN is used to extract the signal photons, where the key parameters of elliptic filter kernel are automatically determined according to the topographic data situation. We compared it with other methods, including local distance statistics (LDS), traditional and modified DBSCAN, traditional and modified ordering points to identify cluster structure (OPTICS), and ATL08 data. Some quantitative indicators, such as recall (R), precision (P), and F-score (F), are used to evaluate its performance. The results show that; 1) the adaptive DBSCAN has the best performance on preserving the vertical structural characteristics of ground objects, and 2) the adaptive DBSCAN in the mean R, P, and F of three land covers (i.e., mountain forest, urban, and water areas) can get up to the maximum are 0.9852, 0.9675, and 0.9761, respectively; followed by ATL08 data with 0.9773, 0.9412, and 0.9536, modified OPTICS with 0.9684, 0.9460, and 0.9586, and modified DBSCAN with 0.9613, 0.9474, and 0.9544; and then OPTICS with 0.9444, 0.9397, and 0.9378, and the DBSCAN with 0.9444, 0.9355, and 0.9554; the last one is LDS with 0.9382, 0.9261, and 0.9422. The proposed method provides an alternative approach for rapid and accurate processing of PPC on complex terrain.

Bioradar systems, in general, refer to radar systems used for the detection of vital signs. These systems hold significant importance across various sectors, particularly in healthcare and surveillance, due to their capacity to provide contactless solutions for monitoring physiological functions. In these applications, the primary challenge lies in the presence of random body movements (BMs), which can significantly hinder the accurate detection of vital signs. To compensate the affected signal in a timely manner, portions of BM must be correctly identified. To address this challenge, this work proposes a solution based on the Density-Based Spatial Clustering of Applications with Noise (DBScan) algorithm to detect the occurrence of BM in radar signals. The main idea of this algorithm is to cluster the radar samples, aiming to differentiate between segments in which the subject is stable and segments in which the subject is moving. Using a dataset involving eight subjects, the proposed method successfully detects three types of body movements: chest movement, body rotation, and arm movement. The achieved results are promising, with F1 scores of 0.83, 0.73, and 0.8, respectively, for the detection of each specific movement type.

As cultural facilities, physical bookstore is an important part of urban infrastructure. Influenced by the development of social economy and the internet, physical bookstores also have become a combination of cultural space and tourism experience. In this case, it is necessary to explore the spatial characteristics and influencing factors of physical bookstores. This study uses Density-Based Spatial Clustering of Applications with Noise (DBSCAN), spatial analysis and geographical detectors to calculate the spatial distribution pattern and factors influencing physical bookstores in national central cities/municipality (hereafter using cities) in western China. Based on spatial data, population density, road density and other data, this study constructed a data set of the influencing factors of physical bookstores, consisting of 11 factors along 6 dimensions for 3 national central cities in western China. The results are as follows: first, the spatial distribution pattern of physical bookstores in Xi'an, Chengdu, and Chongqing is unbalanced. The spatial distribution of physical bookstores in Xi'an and Chongqing is from southwest to northeast and are relatively clustered, while those in Chengdu are relatively discrete. Second, the spatial distribution pattern of physical bookstores has been formed under the influence of different factors. The intensity and significance of influencing factors differ in the case cities. However, in general, the social factor, business factor, the density of research facilities, tourism factor and road density are the main driving factors in the three cities. There is a synergistic relationship between public libraries and physical bookstores. Third, the explanatory power becomes stronger after the interaction between various factors. In Xi'an and Chengdu, the density of communities and the density of research facilities have stronger explanatory power for the dependent variable after interacting with other factors. However, in Chongqing, the traffic factors have stronger explanatory power for the dependent variable after interacting with other factors. The results could provide a practical reference for the sustainable development of physical bookstores and encourage a love of reading among the public. [ABSTRACT FROM AUTHOR]

Synesthesia is a phenomenon where sensory stimuli or cognitive concepts elicit additional perceptual experiences. For instance, in a commonly studied type of synesthesia, stimuli such as words written in black font elicit experiences of other colors, e.g., red. In order to objectively verify synesthesia, participants are asked to choose colors for repeatedly presented stimuli and the consistency of their choices is evaluated (consistency test). Previously, there has been no publicly available and easy-to-use tool for analyzing consistency test results. Here, the R package synr is introduced, which provides an efficient interface for exploring consistency test data and applying common procedures for analyzing them. Importantly, synr also implements a novel method enabling identification of participants whose scores cannot be interpreted, e.g., who only give black or red color responses. To this end, density-based spatial clustering of applications with noise (DBSCAN) is applied in conjunction with a measure of spread in 3D space. An application of synr with pre-existing openly accessible data illustrating how synr is used in practice is presented. Also included is a comparison of synr's data validation procedure and human ratings, which found that synr had high correspondence with human ratings and outperformed human raters in situations where human raters were easily mislead. Challenges for widespread adoption of synr as well as suggestions for using synr within the field of synesthesia and other areas of psychological research are discussed. [ABSTRACT FROM AUTHOR]

Effective management of the high-speed railways (HSR) system requires an in-depth understanding of the HSR stations in the network, e.g., the time-dependent volume distribution. The classification of HSR stations is the scientific basis for transport policymaking and land-use planning. Existing classification methods cannot meet the needs of temporal variation of passenger flow or the refined design and operation of HSR stations. This study adopts the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm to classify HSR stations in different years. Using the data of Jiangsu Province, China, as an example, the time series of arrival and departure passenger flow at HSR stations are clustered via the DBSCAN algorithm, and the HSR stations are clustered into three classes. To determine the hierarchical structure of HSR stations representing the evolution of HSR networks, we use large-scale panel data obtained from mobile phone cellular data across years (July 1–14 from each of the years 2018, 2020, and 2021) to capture and analyze the spatial-temporal evolution characteristics of massive passenger flow at HSR stations. It is indicated that both HSR station hierarchy and passenger flow have the characteristics of spatial-temporal evolution across years, and the classification results are influenced by the geographical positions of cities and HSR layout. Accurate clustering of HSR stations via large-scale actual passenger flow data enables railway authorities and operators to identify critical nodes for efficient HSR network performance. The resulting classification would contribute to an in-depth understanding of the evolution characteristics of passenger flow in different years. [ABSTRACT FROM AUTHOR]

In the analysis of rock slope stability and rock mass hydraulics, the dominant partitioning of discontinuities of rock masses is a very important concept, and it is still a key for establishing the three-dimensional (3-D) network model of random discontinuities. The traditional graphical analysis method is inadequate and greatly influenced by subjective experience. A new method using density-based spatial clustering of applications with noise (DBSCAN) algorithm is proposed for the dominant partitioning of discontinuities of rock mass. In the proposed method, we do not need to determine the centers of every cluster before clustering, and the acnodes or outliers can be eliminated effectively after clustering. Firstly, the spatial coordinate transformation of the discontinuity occurrence is carried out and the objective function is established by using the sine value of the angle of the unit normal vector as the similarity measure standard. The DBSCAN algorithm is used to establish the optimal clustering centers by searching the global optimal solution of the objective function, and the fuzzy C-means clustering algorithm is optimized and the mathematical model of the advantage grouping of rock discontinuities is established. The new method and the fuzzy C-means method are compared and verified by using the artificially randomly generated discontinuity occurrence data. The proposed method is a better method than the fuzzy C-means method in general cases, and it can provide more accurate results by eliminating the acnodes or outliers. Finally, the proposed method is applied to discontinuity orientation partition data at Maji dam site, Nujiang River, and there is good agreement with the in situ measurement. [ABSTRACT FROM AUTHOR]

This article presents different methods for noninvasive validation and diagnostics of contactless devices. The radio frequency systems studied here operate at 13.56 MHz. When manufacturing these systems in volume, it is essential to separate the fully functional devices from the totally defective ones or even from those communicating but have anomalies that will lead to a significant reduction of their lifetime. This article compares two noninvasive methods, one based on impedance measurements and the other on impulse response measurements. The advantages and drawbacks of these methods are presented and compared to their use in large-scale manufacturing. In addition to the proposed methods, this article describes two decision-making methodologies based on machine learning. This article compares also both measurement methods and machine learning tools. A robustness study shows the limitations of the employed techniques

Pavement friction plays a crucial role in ensuring the safety of road networks. Accurately assessing friction levels is vital for effective pavement maintenance and for the development of management strategies employed by state highway agencies. Traditionally, friction evaluations have been conducted on a case-by-case basis, focusing on specific road sections. However, this approach fails to provide a comprehensive assessment of friction conditions across the entire road network. This paper introduces a hybrid clustering algorithm, namely the combination of density-based spatial clustering of applications with noise (DBSCAN) and Gaussian mixture model (GMM), to perform pavement-friction performance ratings across a statewide road network. A large, safety-oriented dataset is first generated based on the attributes possibly contributing to friction-related crashes. One-, two-, and multi-dimensional clustering analyses are performed to rate pavement friction. After using the Chi-square test, six ratings were identified and validated. These ratings are categorized as (0, 20], (20, 25], (25, 35], (35, 50], (50, 70], and (70, ∞). By effectively capturing the hidden, intricate patterns within the integrated, complex dataset and prioritizing friction-related safety attributes, the hybrid clustering algorithm can produce pavement-friction ratings that align effectively with the current practices of the Indiana Department of Transportation (INDOT) in friction management. [ABSTRACT FROM AUTHOR]

The majority of photovoltaic (PV) systems in the Netherlands are small scale, installed on rooftops, where the lack of onsite global tilted irradiance (GTI) measurements and the frequent presence of shadow due to objects in the close vicinity oppose challenge in their monitoring process. In this study, a new algorithmic tool is introduced that creates a reference data‐set through the combination of data‐sets of the unshaded PV systems in the surrounding area. It subsequently compares the created reference data‐set with the one of the PV system of interest, detects any energy loss and clusters the distinctive loss due to shadow, created by the surrounding objects. The new algorithm is applied successfully to a number of different cases of shaded PV systems. Finally, suggestions on the unsupervised use of the algorithm by any monitoring platform are discussed, along with its limitations algorithm and suggestions for further research. [ABSTRACT FROM AUTHOR]

Semantic segmentation of large unstructured 3D point clouds is important problem for 3D object recognition which in turn is essential to solving more complex tasks such as scene understanding. The problem is highly challenging owing to large scale of data, varying point density and localization errors of 3D points. Nevertheless, with recent successes of deep neural network architectures to solve complex 2D perceptual problems, several researchers have shown interest to translate the developed 2D networks to 3D point cloud segmentation by a prior voxelization step for an explicit neighborhood representation. However, such a 3D grid representation loses the fine details and inherent structure due to quantization artifacts. For this purpose, this paper proposes an approach to performing semantic segmentation of 3D point clouds by exploiting the idea of super-point based graph construction. The proposed architecture is composed of two cascaded modules including a light-weight representation learning module which uses unsupervised geometric grouping to partition the large-scale unstructured 3D point cloud and a deep context aware sequential network based on long short memory units and graph convolutions with embedding residual learning for semantic segmentation. The proposed model is evaluated on two standard benchmark datasets and achieves competitive performance with the existing state-of-the-art datasets. The code and the obtained results have been made public at https://github.com/saba155/DCARN. [ABSTRACT FROM AUTHOR]

Copyright of Przegląd Elektrotechniczny is the property of Przeglad Elektrotechniczny 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.)

For the problems existing in the traditional single discontinuity (structural plane) based clustering model, such as the risk of misclassification or omission and the inability to identify noise and isolated values, a dominant partitioning method of rock mass discontinuity based on selective clustering ensemble using density-based spatial clustering of applications with noise (DBSCAN) algorithm is proposed. Firstly, the spatial coordinate transformation is performed with the attitude of discontinuity, and the sine of the angle between the unit normal vectors is defined as similarity measurement. Then, a certain number of different base clusters are constructed based on the DBSCAN algorithm, with the selective clustering ensemble technology, some excellent base clusters are selected. Finally, the consistent ensemble technology is used to fuse these base clusters to generate a highly reliable selective clustering ensemble result. The DIPS software data set and the discontinuity survey result in the dam site area of Songta hydropower station are used to test the feasibility and effectiveness of the proposed method. The research results show that the clustering effect of the proposed method is significantly better than that of common clustering algorithms. The clustering results are objective and reasonable. It not only effectively identifies noise and isolated values, but also overcomes the shortcomings of over-segmentation or under-segmentation of the single discontinuity based clustering model. The research results are valuable in accurately determining the dominant group of discontinuity.

Dengue Fever (DF) is a common vector-borne disease with catastrophic health implications. DF prediction modelling is a challenging task, although technologies such as Geographical Information Systems (GIS) and spatial statistics have improved our understanding of dengue dynamics. In this paper, we create a robust data analysis model to (i) provide a better understanding of confirmed dengue fever cases despite missing data, (ii) obtain better insights into risk factors associated with confirmed cases, and (iii) by means of machine learning, create clusters of patients with comparable characteristics. The last was accomplished with a self-organizing feature map (SOFM) and the densitybased spatial clustering of applications with noise (DBSCAN). The approaches used to classify confirmed cases were: Decision Tree, k-nearest neighbours, Random Forest, AdaBoost, Support Vector Classification (SVC), CatBoost, and Naive Bayes. The CatBoost classifier achieved the best accuracy for the analysis of confirmed cases. Spatial analysis was conducted using the ordinary least square (OLS) and geographically weighted regression (GWR) models to identify high-risk areas. SOM can group patients with similar features into clusters, then DBSCAN detects and retrieves six clusters from this data. The clustering of confirmed cases increases CatBoost’s modelling prediction accuracy and reveals complex factors that influence prediction accuracy. Because confirmed cases in each cluster have different features, CatBoost is applied to each cluster individually to improve the prediction accuracy. Variable values in each cluster are analysed to clarify the confirmed cases of a specific subset of DF incidents. Overall, OLS outperforms GWR when identifying hotspot areas. The proposed novel, data-driven and machine-learningbased strategy facilitates the understanding and identification of patterns associated with confirmed DF cases. The study’s findings can be utilized to cluster historical patient data into groups or subgroups sharing similar variables. Using identifiable patient clusters rather than raw history data improves the model accuracy provided by CatBoost. [ABSTRACT FROM AUTHOR]

In this article, we propose an adaptive superpixel generation algorithm for synthetic aperture radar (SAR) imagery, which is implemented based on fast density-based spatial clustering of applications with noise (DBSCAN) clustering and superpixel merging with edge penalty. The superpixel generation algorithm consists of two stages, i.e., fast pixel clustering and superpixel merging. In the clustering stage, we define a new adaptive pixel dissimilarity measure for SAR image and then optimize the DBSCAN strategy, which considers the edge information and can achieve rapid clustering. In the merging stage, based on the initial superpixels, a new superpixel dissimilarity measure is defined, which can merge the small local superpixels into their neighborhood superpixels, making the final superpixel segmentation results compact and regular. Experimental results on two simulated and two real SAR images demonstrate that our method outperforms the state-of-the-art superpixel generation methods in terms of both efficiency and accuracy. The superpixel segmentation accuracy of our method is 5–10% higher and the time cost is 10–40% lower than other methods. Since the superpixel segmentation result can be used as a preprocessing stage for the SAR data interpretation applications, superpixel-based and pixel-based classification results with two real SAR images are also used for comparison, which can validate the advantages of our proposed method.

In this article, we propose a method for instance segmentation of individual grains from a terrestrial laser scanning point cloud representing a mountain river bed. The method was designed as a classification followed by a segmentation approach. The binary classification into either points representing river bed or grains is performed using the random forest algorithm. The point cloud is classified based only on geometrical features calculated for a local, spherical neighborhood. A multisize neighborhood approach was used together with the feature selection method that is based on correlation analysis. The final classification was performed using a set of features calculated for the neighborhood size of 5, 15, and 20 cm. The achieved classification results have the overall accuracy of 85–95%, depending on the test site. The segmentation is performed using the density-based spatial clustering of applications with noise algorithm in order to cluster the point cloud based on Euclidean distances between points. The performed experiments showed that the proposed method enables us to correctly delineate 67–88% of grains, depending on the test site. However, the resulting point cloud based completeness expressed as Jaccard index is similar for each of the test sites and is approximately 88%. Moreover, the proposed method proved that it is robust to the shadowing effect.

Intrusion detection and classification have gained significant attention recently due to the increased utilization of networks. For this purpose, there are different types of Network Intrusion Detection System (NIDS) approaches developed in the conventional works, which mainly focus on identifying the intrusions from the datasets with the help of classification techniques. Still, it is limited by the significant problems of inefficiency in handling large dimensional datasets, high computational complexity, false detection, and more time consumption for training the models. To solve these problems, this research intends to develop an innovative clustering-based classification methodology to precisely detect intrusions from the different types of IDS datasets. Here, the most recent and extensively used IDS datasets such as NSL-KDD, CICIDS, and Bot-IoT have been employed for detecting intrusions. Data preprocessing has been performed to normalize the dataset to eliminate irrelevant attributes and organize the features. Then, the data separation is applied by forming the clusters by using an intelligent Anticipated Distance-based Clustering (ADC) incorporated with the Density-Based Spatial clustering of applications with noise (DBScan) algorithm. It helps to find the distance and density measures for grouping the attributes into the clusters, which increases the efficiency of classification. Here, the most suitable optimal parameters are selected using the Perpetual Pigeon Galvanized Optimization (PPGO) technique. The extracted features are used for training and testing the dataset samples. Consequently, the Likelihood Naïve Bayes (LNB) classification approach is implemented to accurately predict the classified label as to whether normal or attack. During the evaluation, the performance of the proposed IDS framework is validated and compared using various evaluation metrics. The results show that the proposed ADC-DBScan-LNB model outperforms the other techniques with improved performance outcomes.

In recent years, visible light positioning (VLP) systems have attracted considerable attention because they do not require additional infrastructures. However, most existing researches on the VLP ignore the impact of wall diffuse reflection, which can lead to the dramatical decrease of the position accuracy performance near the indoor walls and corners. In this paper, we design an indoor VLP system with one single light-emitting diode (LED) and a rotatable photodetector (PD), and then propose an indoor VLP algorithm based on machine learning (ML) methods with concern for the indoor reflection of the optical propagation. The proposed positioning process is implemented via two stages: area classification and positioning. During the area classification stage, by using the random forest (RF) algorithm, the entire room is divided into one interior area and four wall or corner zones. In the interior area, the rotatable PD is directly used to determine the target location. In the four wall or corner zones, a hybrid positioning algorithm based on the extreme learning machine (ELM) and the density-based spatial clustering of applications with noise (DBSCAN) is developed to improve localization accuracy near the indoor walls and corners. Simulation results show that by using the proposed indoor VLP system with the rotatable PD and the hybrid algorithm, the maximum and averaged positioning errors of wall or corner zones drop from 137.96 cm and 15.63 cm, to 38.34 cm and 1.43 cm, respectively, and the averaged positioning error of the whole room decreases from 11.97 cm to 1.74 cm.

In the analysis of rock slope stability and rock mass hydraulics, the dominant partitioning of discontinuities of rock masses is a very important concept, and it is still a key for establishing the three-dimensional (3-D) network model of random discontinuities. The traditional graphical analysis method is inadequate and greatly influenced by subjective experience. A new method using density-based spatial clustering of applications with noise (DBSCAN) algorithm is proposed for the dominant partitioning of discontinuities of rock mass. In the proposed method, we do not need to determine the centers of every cluster before clustering, and the acnodes or outliers can be eliminated effectively after clustering. Firstly, the spatial coordinate transformation of the discontinuity occurrence is carried out and the objective function is established by using the sine value of the angle of the unit normal vector as the similarity measure standard. The DBSCAN algorithm is used to establish the optimal clustering centers by searching the global optimal solution of the objective function, and the fuzzy C-means clustering algorithm is optimized and the mathematical model of the advantage grouping of rock discontinuities is established. The new method and the fuzzy C-means method are compared and verified by using the artificially randomly generated discontinuity occurrence data. The proposed method is a better method than the fuzzy C-means method in general cases, and it can provide more accurate results by eliminating the acnodes or outliers. Finally, the proposed method is applied to discontinuity orientation partition data at Maji dam site, Nujiang River, and there is good agreement with the in situ measurement.

This article provides a new benchmark dataset for 3-D point cloud classification in which the manually labeled human activity data exceeds 100 point clouds per frame and is capable of meeting the training needs for data-intensive learning approaches. In this study, a case study is considered for evaluating the benchmark using a deep long short-term memory (LSTM) neural network, which demonstrated a significant performance improvement over the state-of-the-art human activity recognition (HAR) area. To date, numerous types of collection devices have been used in the recognition of human activities. However, due to the scarcity of training data, the task of 3-D point cloud labeling has not yet made significant progress. To overcome this challenge, it is aimed to deduce this data requirements gap, allowing deep-learning methods to reach their full potential in 3-D point cloud tasks. The dataset used for this process is comprised of dense point clouds acquired with the static ground sensor by the NodeNs company-supported multiple input multiple output (MIMO) radar (NodeNs ZERO 60 GHz IQ radar). It contains multiple types of human being data ranging from one to four individuals and encompasses a range of human action scenarios, including standing, sitting, picking up, falling, and walking. Furthermore, it also investigated sensor locations and requirements for human being data collection that is from a single subject to multiple subjects, as well as identified and analyzed various sensing devices and applications that collect activity data. In this regard, a thorough study is conducted on several benchmark datasets, examining sensors, characteristics, activity categories, and other data. Finally, it compares and analyzes the activity recognition methods used in several benchmark datasets based on the current study. Unlike existing devices, the new NodeNs sensor provides more accessible and straightforward point cloud data to capture human movement information. Depending on an advanced detection algorithm to process point cloud data, it achieved more than 95% accuracy on the benchmark dataset. [ABSTRACT FROM AUTHOR]

Pavement friction plays a crucial role in ensuring the safety of road networks. Accurately assessing friction levels is vital for effective pavement maintenance and for the development of management strategies employed by state highway agencies. Traditionally, friction evaluations have been conducted on a case-by-case basis, focusing on specific road sections. However, this approach fails to provide a comprehensive assessment of friction conditions across the entire road network. This paper introduces a hybrid clustering algorithm, namely the combination of density-based spatial clustering of applications with noise (DBSCAN) and Gaussian mixture model (GMM), to perform pavement-friction performance ratings across a statewide road network. A large, safety-oriented dataset is first generated based on the attributes possibly contributing to friction-related crashes. One-, two-, and multi-dimensional clustering analyses are performed to rate pavement friction. After using the Chi-square test, six ratings were identified and validated. These ratings are categorized as (0, 20], (20, 25], (25, 35], (35, 50], (50, 70], and (70, ∞). By effectively capturing the hidden, intricate patterns within the integrated, complex dataset and prioritizing friction-related safety attributes, the hybrid clustering algorithm can produce pavement-friction ratings that align effectively with the current practices of the Indiana Department of Transportation (INDOT) in friction management.

In recent years, visible light positioning (VLP) systems have attracted considerable attention because they do not require additional infrastructures. However, most existing researches on the VLP ignore the impact of wall diffuse reflection, which can lead to the dramatical decrease of the position accuracy performance near the indoor walls and corners. In this paper, we design an indoor VLP system with one single light-emitting diode (LED) and a rotatable photodetector (PD), and then propose an indoor VLP algorithm based on machine learning (ML) methods with concern for the indoor reflection of the optical propagation. The proposed positioning process is implemented via two stages: area classification and positioning. During the area classification stage, by using the random forest (RF) algorithm, the entire room is divided into one interior area and four wall or corner zones. In the interior area, the rotatable PD is directly used to determine the target location. In the four wall or corner zones, a hybrid positioning algorithm based on the extreme learning machine (ELM) and the density-based spatial clustering of applications with noise (DBSCAN) is developed to improve localization accuracy near the indoor walls and corners. Simulation results show that by using the proposed indoor VLP system with the rotatable PD and the hybrid algorithm, the maximum and averaged positioning errors of wall or corner zones drop from 137.96 cm and 15.63 cm, to 38.34 cm and 1.43 cm, respectively, and the averaged positioning error of the whole room decreases from 11.97 cm to 1.74 cm. [ABSTRACT FROM AUTHOR]

To solve the problem of low automatic number plate recognition (ANPR) data integrity and low completion accuracy of incomplete traffic data, which affects the quality and utilization of ANPR data, this paper proposed a model for estimating the travel time of the road link that considers the heterogeneity of the driving styles. The travel time of historical road sections in the road network was extracted from ANPR data. The driving crowd was clustered through density-based spatial clustering of applications with noise (DBSCAN) based on the time slot, the number of trips, and the travel time. To avoid the excessive data difference between different classes and the distortion of the complement data, the Lagrange interpolation method was adopted to complement the missing road link travel time within each cluster. Taking Ningbo city in China as an example, the travel time completion accuracies of the proposed method and the direct interpolation method were compared. The results show that the interpolation method considering the heterogeneity of driving styles is more sufficient to increase the completion accuracy by 37.4% compared with the direct interpolation manner. The comparison result verifies the effectiveness of the proposed method and can provide more reliable data support for the construction of the transportation system. [ABSTRACT FROM AUTHOR]

The development of cloud computing and big data analysis has given rise to various disaster prediction methods. To reduce the probability of fire accidents, it is critical to predict the fire risk by mining the massive historical data on fire. Considering the advantages of MapReduce, a cloud computing method, in parallel processing of data, this paper puts forward a novel prediction method for fire risk that mines the association rules in the time domain. Firstly, the risk of disaster-causing factors and the ability of disaster-reducing factors were evaluated. Based on the evaluation results, an evaluation index system was constructed for fire risk, and the indices were quantified through proper weighting. Facing the historical fire data, the authors designed the spatiotemporal density-based spatial clustering of applications with noise (spatiotemporal DBSCAN), and quantitatively evaluated fire risk by the association rule mining algorithm based on time domain partition (TDP). The effectiveness of our method in fire risk prediction was verified through experiments. The research results provide reference for the risk prediction of other disasters.

Seismic reflections are crucial for obtaining information about subsurface structures and lithologies for oil and gas exploration. Several techniques have recently been introduced which improve the visualization of subsurface structures, lithologies, and facies. This article proposes a novel method of seismic reflection identification through the integration of continuous wavelet transform (CWT) and density-based spatial clustering of applications with noise (DBSCAN). Here, a three-layer geological model is adopted. Initially, 2D seismic reflection data with 5%, 8%, and 10% Gaussian noise are generated. Later, the DBSCAN algorithm is applied to 2D noise seismic data, and clusters are generated at their respective times for each reflector. Next, to confirm and validate the results of DBSCAN, CWT is executed on the cluster data set. Based on our results of CWT, the true representation of seismic data with minimum noise in the time domain is achieved. The successful integration of DBSCAN and CWT is achieved in terms of identification of true seismic reflections as localized anomalous zones at 0.8 s, 1 s, and 1.07 s, which exactly match the geological model of this study. [ABSTRACT FROM AUTHOR]

In recent years, several clustering algorithms have been proposed with the aim of mining knowledge from streams of data generated at a high speed by a variety of hardware platforms and software applications. Among these algorithms, density-based approaches have proved to be particularly attractive, thanks to their capability of handling outliers and capturing clusters with arbitrary shapes. The streaming setting poses additional challenges that need to be addressed as well: data streams are potentially unbounded and affected by concept drift, i.e., a modification over time in the underlying data generation process. In this article, we propose temporal streaming fuzzy density-based spatial clustering of applications with noise (TSF-DBSCAN), a novel fuzzy clustering algorithm for streaming data. TSF-DBSCAN is an extension of the well-known DBSCAN algorithm, one of the most popular density-based clustering approaches. Fuzziness is introduced in TSF-DBSCAN to model the uncertainty about the distance threshold that defines the neighborhood of an object. As a consequence, TSF-DBSCAN identifies clusters with fuzzy overlapping borders. A fading model, which makes objects less relevant as they become more remote in time, endows TSF-DBSCAN with the capability of adapting to evolving data streams. The integration of the model in a two-stage approach ensures computational and memory efficiency: during the online stage, continuously arriving objects are organized in proper data structures that are later exploited in the offline stage to determine a fine-grained partition. An extensive experimental analysis on synthetic and real-world datasets shows that TSF-DBSCAN yields competitive performance when compared to other clustering algorithms recently proposed for streaming data. [ABSTRACT FROM AUTHOR]

Accurate point matching is widely used, and it is a critical and challenging process in feature-based image registration. To improve feature matching accuracy on putative matches with heavy outliers and similar local structures, an accurate and robust feature point matching algorithm based on minimum relative motion entropy (MRME) is proposed, in which the relative motion between the putative matches and their K-nearest neighbors is formulated. Based on the relative motion clustering result, the relative motion entropy is defined to find the coincident relative motions. According to relative motions with MRME, the outliers are removed in a two-stage feature match strategy. With quasi-linear time complexity, outliers with random or irregular relative motion are removed efficiently and accurately, while inliers with coincident relative motion are retained. Three data sets with repetitive patterns, viewpoint changes, low overlapping areas, and local deformations are used to demonstrate the performance of the proposed algorithm. MRME is shown to be more robust and accurate than ten state-of-the-art feature matching algorithms. [ABSTRACT FROM AUTHOR]

Traffic information collection is an important foundation for intelligent transportation systems. In this paper, 3D Light Detection And Ranging (LiDAR) is deployed in the roadside of urban environments to collect vehicle and pedestrian information. A background filtering algorithm, including a mean background modeling to build a background map and a background difference method to filter static background noise points, is proposed for roadside fixed LiDAR facilities. Background points are filtered through the difference between data frames and a multi-level background map, and then there are still a small number of noise points. Aiming to reduce the noise points, a hierarchical maximum density clustering of applications with noise (HMDCAN) algorithm, utilizing both density clustering and hierarchical clustering, is proposed to effectively achieve both noise point filtering and target recognition. We verify our methods in a facility with a 16-channel LiDAR in which background filtering and target recognition are tested with different scenarios, and the accuracy rate is over 97%. [ABSTRACT FROM AUTHOR]

Accurate prediction of train delay is an important basis for the intelligent adjustment of train operation plans. This paper proposes a train delay prediction model that considers the delay propagation feature. The model consists of two parts. The first part is the extraction of delay propagation feature. The best delay classification scheme is determined through the clustering method of delay types for historical data based on the density-based spatial clustering of applications with noise algorithm (DBSCAN), and combining the best delay classification scheme and the k-nearest neighbor (KNN) algorithm to design the classification method of delay type for online data. The delay propagation factor is used to quantify the delay propagation relationship, and on this basis, the horizontal and vertical delay propagation feature are constructed. The second part is the delay prediction, which takes the train operation status feature and delay propagation feature as input feature, and use the gradient boosting decision tree (GBDT) algorithm to complete the prediction. The model was tested and simulated using the actual train operation data, and compared with random forest (RF), support vector regression (SVR) and multilayer perceptron (MLP). The results show that considering the delay propagation feature in the train delay prediction model can further improve the accuracy of train delay prediction. The delay prediction model proposed in this paper can provide a theoretical basis for the intelligentization of railway dispatching, enabling dispatchers to control delays more reasonably, and improve the quality of railway transportation services. [ABSTRACT FROM AUTHOR]

Detecting and tracking the position of multiple targets indoors is a challenging measurement problem due to the inherent difficulty to cluster correctly the sensor data associated to a given target and to track the position of each cluster with adequate accuracy. This problem is critical especially in rooms filled with fixed or moving objects hampering target detection and whenever the paths of different targets cross one another. In this paper, a robust Multiple Targets Tracking (MTT) algorithm exploiting the clouds of points collected from a mmWave-FMCW radar is presented. The proposed solution consists of four main steps. First, the possible outliers of a raw radar data set are removed using a neural network model. Next, the cleaned-up radar data are clustered using the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm. Then, a Kalman Filter (KF) is used to track the position of the centroid of each cluster. Finally, a Structured Branching Multiple Hypothesis Testing (SBMHT) algorithm is applied and updated over reasonably short time intervals to decide which detected tracks are supposed to be confirmed and which ones instead should be discarded. The proposed MTT technique was validated experimentally using the data sets collected from a 60-GHz TI IWR6843 radar platform. The reported results show that the developed algorithm, if properly tuned, is faster and returns more accurate results than other MTT techniques. In particular, the percentage of detection errors is negligible and the planar positioning accuracy is within about 30 cm with 90% probability when up to five targets move freely within the same room. [Display omitted] • A Multiple Targets Tracking (MTT) algorithm exploiting indoor radar data is proposed. • Robust outlier removal and clustering based on a Neural Network and DBSCAN. • A Structured Branching Multiple Hypothesis Testing (SBMHT) algorithm used to confirm/discard tracks. • Planar positioning accuracy (with a 60-GHz radar) is within ±30 cm when 5 people move in the room. • Probability of detection errors and processing latency lower than other MTT algorithms. [ABSTRACT FROM AUTHOR]

Based on linear back projection (LBP) algorithm, density-based spatial clustering of applications with noise (DBSCAN) algorithm and image fusion technique, a new image reconstruction method of capacitively coupled electrical impedance tomography (CCEIT) is proposed in this work. LBP is used to obtain the original image of the real part and the original image of the imaginary part. The gray level thresholds of the two original images are determined by DBSCAN clustering algorithm and the initial image of the real part and the initial image of the imaginary part are obtained by gray level threshold filtering, respectively. Finally, the two initial images are fused and the final image is obtained. Image reconstruction experiments are carried out with a 12-electrode CCEIT system prototype. The experimental results verify the effectiveness of the proposed image reconstruction method. Experimental results show that the quality of the final image is satisfactory. Compared with the conventional electrical tomography (ET) image reconstruction methods, the new proposed image reconstruction method needs less prior knowledge or manual intervention and realizes more effective usage of the impedance information. The research results also indicate that the DBSCAN clustering algorithm is an effective way to determine the gray level thresholds of the original images and the mean square error (MSE) is a reasonable criterion for image fusion. Meanwhile, LBP + DBSCAN is an effective algorithm for image reconstruction and image fusion technique is an effective way to utilize the whole impedance information of the conductive gas–liquid two-phase flow. [ABSTRACT FROM AUTHOR]

Rule-based models are applicable to model the behavior of complex and nonlinear systems. Due to limited experience and randomness involving constructing information granules, an insufficient credible rules division could reduce the model’s accuracy. This paper proposes a new rule-based modeling approach, which utilizes density-based spatial clustering of applications with noise (DBSCAN)-based information granules to construct the rules. First, bear in mind the advantages of density-based clustering, DBSCAN is proposed to generate data structures. Based on these data structures, two rule-based models are constructed: 1) models using DBSCAN clusters to construct granules and rules directly and 2) models generating subgranules in each DBSCAN cluster for rule formation. Experiments involving these two models are completed, and obtained results are compared with those generated with a traditional model involving fuzzy C-means-based granules. Numerical results show that the rule-based model, which builds rules from subgranules of DBSCAN structures, performs the best in analyzing system behaviors. [ABSTRACT FROM AUTHOR]

To solve the problem of low automatic number plate recognition (ANPR) data integrity and low completion accuracy of incomplete traffic data, which affects the quality and utilization of ANPR data, this paper proposed a model for estimating the travel time of the road link that considers the heterogeneity of the driving styles. The travel time of historical road sections in the road network was extracted from ANPR data. The driving crowd was clustered through density-based spatial clustering of applications with noise (DBSCAN) based on the time slot, the number of trips, and the travel time. To avoid the excessive data difference between different classes and the distortion of the complement data, the Lagrange interpolation method was adopted to complement the missing road link travel time within each cluster. Taking Ningbo city in China as an example, the travel time completion accuracies of the proposed method and the direct interpolation method were compared. The results show that the interpolation method considering the heterogeneity of driving styles is more sufficient to increase the completion accuracy by 37.4% compared with the direct interpolation manner. The comparison result verifies the effectiveness of the proposed method and can provide more reliable data support for the construction of the transportation system.