Point Neighborhood Research Articles

Improved Gaussian processes linear JPDA filter for multiple extended targets tracking in dense clutter

To track multiple extended targets in dense clutter, an improved Gaussian processes linear joint probabilistic data association (IGP-LJPDA) method is proposed. This method consists of two stages. In the first stage, measurements are associated with targets, and pseudo-measurements are constructed. By integrating linear JPDA with Gaussian processes, probabilities for each measurement's assignment to each target are determined. Following this, a novel pseudo-measurement model is devised by combining the marginal probability of each target with measurements belonging to their respective basic point neighborhood, enabling more efficient state updates. In the second stage, pseudo-measurements are associated with the contour, and states of extended targets are updated accordingly. By associating pseudo-measurements with Gaussian processes basic points of each target, the algorithm achieves globally optimal association. The proposed algorithm demonstrates a notable improvement in Intersection over Union (IOU) metrics in environments with significantly higher clutter rates compared to existing methods.

Study on spray characteristics of biodiesel alternative fuels for in-cylinder environment of diesel engine

This paper presents an experimental study of spray characterization of MD-NH (a blend of methyl decanoate and n-heptane in a molar ratio of 1:1 to characterize biodiesel) in an in-cylinder environment of diesel engines to provide a reference for the experimental study of engines in advanced combustion mode. Based on the Peng-Robinson state equation, the thermodynamic model of real fluid was established, and the influence law of ambient temperature and pressure on the diffusion characteristics of fuel flow was analyzed. Based on the constant volume combustion bomb experiments, the variations of spray penetration distance, spray cone angle, spray projection area, spray air entrainment mass, and spray fragmentation forms in different critical environment conditions were studied. The results show that the transport properties of the fuel change abruptly within the critical point neighborhood of the fuel. As the ambient temperature increases, near the critical temperature, the thermal conductivity of biodiesel decreases and then increases; density and kinematic viscosity decrease at an accelerated rate; and surface tension increases to a maximum and then disappears sharply. The diffusion coefficient decreases sharply when the ambient pressure reaches the critical pressure. The spray characteristics of biodiesel in sub/supercritical conditions were different. Compared with the subcritical environment, the biodiesel spray penetration distance decreases, the spray cone angle increases, the spray projection area decreases, and the air entrainment mass increases in the supercritical atmosphere, which induces a gas-liquid interface mixing layer and promotes the mixing of biodiesel with the atmosphere gas. The experimental data in this paper fill the gap in the study of biodiesel spray characteristics in supercritical environments and provide a basis for high-fidelity numerical simulation of the spray model.

Data-driven multi-step energy consumption forecasting with complex seasonality patterns and exogenous variables: Model accuracy assessment in change point neighborhoods

Energy forecasting tools became a significant scope of application for time series modeling due to the specific challenges in energy trading — the forecast of consumption for the whole next trading day based on the limited data availability at the forecast origin. The research article addresses the scope of high-frequency time series data, multiple seasonal patterns, exogenous variables, and nonstationary properties in a multi-step forecast horizon tasks. The contribution of the research is the introduction of a machine- and deep-learning-based data-driven approach for multi-output time series forecasting and mainly an introduction of the new evaluation metric called the Change Point Neighborhood Error (CPNE). The purpose of the metric is to provide a distinctive measure of forecasting accuracy of the proposed models in parts of the time series where a change point or a data drift emerges. The experimental findings indicate a notable improvement in accuracy achieved by machine and deep learning models, resulting in a substantial reduction of the mean absolute percentage error (MAPE) by approximately 45 % compared to the optimal statistical model across both datasets used, and also in terms of change point neighborhood error in comparison to statistical models due to their requirement for stationary data input. Deep learning models may be a viable alternative to machine learning approaches; however, deep learning models require long input sequences for accurate forecasting, whereas machine learning methods require shorter input sequences and can benefit more from feature engineering.

Edge Consistency Feature Extraction Method for Multi-Source Image Registration

Multi-source image registration has often suffered from great radiation and geometric differences. Specifically, grayscale and texture from similar landforms in different source images often show significantly different visual features, and these differences disturb the corresponding point extraction in the following image registration process. Considering that edges between heterogeneous images can provide homogeneous information and more consistent features can be extracted based on image edges, an edge consistency radiation-change insensitive feature transform (EC-RIFT) method is proposed in this paper. Firstly, the noise and texture interference are reduced by preprocessing according to the image characteristics. Secondly, image edges are extracted based on phase congruency, and an orthogonal Log-Gabor filter is performed to replace the global algorithm. Finally, the descriptors are built with logarithmic partition of the feature point neighborhood, which improves the robustness of the descriptors. Comparative experiments on datasets containing multi-source remote sensing image pairs show that the proposed EC-RIFT method outperforms other registration methods in terms of precision and effectiveness.

FEASIBILITY AND SAFETY OF A FIELD CARE CLINIC AS AN ALTERNATIVE AMBULANCE DESTINATION DURING THE COVID-19 PANDEMIC.

Anticipating an increased utilization of healthcare facilities during the COVID-19 surge, the San Francisco Department of Public Health developed a plan to deploy neighborhood-based Field Care Clinics (FCCs) that would decompress emergency departments by serving patients with low acuity complaints. These clinics would receive patients directly from the Emergency Medical Services (EMS) system. Transports were initiated by a paramedic-driven protocol, originally by EMS crews and later by the Centralized Ambulance Destination Determination (CADDiE) System. In this study, we evaluated the outcomes of EMS patients who were transported to the FCC, specifically as to whether they required subsequent transfer to the emergency department. We performed a retrospective study of all patients transported to the Bayview-Hunters Point (BHP) neighborhood FCC by EMS between April 11th, 2020, and December 16th, 2020. Descriptive statistics and Chi-Square Tests were used to analyze patient data. In total, 35 patients (20 men, 15 women, average age of 50.9 years) were transported to the FCC. Of these, 16 were Black/African American, 7 were White, 3 were Asian, with 9 identifying as of other races and 9 of Hispanic ethnicity. Twenty-three of these transports resulted from a CADDiE recommendation. Approximately half (n=20) of calls originated within the BHP neighborhood. The most frequent patient complaint was "Pain." Of patients transported to the FCC, 23 were treated and discharged. The 12 remaining patients required hospital transfer, with 3 being discharged after receiving treatment in the emergency department and 9 requiring hospital admission, psychiatric, or sobering services. The likelihood of hospital transfer did not significantly vary by sex (p=0.41), 9-1-1 call origination relative to BHP neighborhood (p=0.92), or CADDiE recommendation (p=0.51). Three-fourths of patients who required subsequent hospital transfer were admitted or required specialized services, suggesting that the FCC was viable for managing low acuity conditions. However, the underutilization of the FCC by EMS as a transport destination and a high hospital transfer rate indicates training and protocol refinement opportunities. Despite the small cohort size, this study demonstrates that an FCC alternative care site can act as a viable source for urgent and emergency care during a pandemic.

Assessing the impact of 3D point neighborhood size selection on unsupervised spall classification with 3D bridge point clouds

The performance of point cloud-based defect classification algorithms depends on the quality of the computed geometric features, which in turn is strongly affected by the selection of the neighborhood size of local 3D points. Many existing algorithms select a single scene-specific value as the neighborhood size parameter and apply it uniformly to all 3D points while computing features. By doing so, some defect features end up being smoothened out at locations where the selected neighborhood size is larger than the optimal choice for those points. Conversely, geometric features might not always capture the local 3D structure if they are calculated based on less than optimal number of neighbors. This paper investigates and assesses the relationship between neighborhood size selection and the performance of point cloud-based unsupervised spall classification algorithms in a quantitative manner. Among the presented neighborhood selection approaches, an entropy-based approach that incorporates a tailored optimal neighborhood size for every point in a point cloud, resulted in a significant improvement over the performance of current state-of-the-art approaches. The performed quantitative study also demonstrated the robustness of this approach to variables, such as subsampling percentage, maximum neighborhood size and different noise levels. The implemented research testbed is comprised of statistically significant number of spall defect datasets generated from five different bridges. The outcome of this research is expected to improve the reliability of point cloud-based condition assessment for concrete bridges.

CrackEmbed: Point feature embedding for crack segmentation from disaster site point clouds with anomaly detection

Laser-scanned point clouds can be used to represent the 3D as-damaged condition of building structures in a post-disaster scenario. Performing crack detection from the acquired point clouds is a critical component of disaster relief tasks such as structural damage assessment and risk assessment. Crack detection methods based on intensity or normals commonly result in noisy detections. On the other hand, deep learning methods can achieve higher accuracy but require a large dataset of annotated cracks. This research proposes an unsupervised learning framework based on anomaly detection to segment out cracked regions from disaster site point clouds. First, building components of interest are extracted from the point cloud scene using region growing segmentation. Next, a point-based deep neural network is used to extract discriminative point features using the geometry of the local point neighborhood. The neural network embedding, CrackEmbed, is trained using the triplet loss function on the S3DIS dataset. Then, an anomaly detection algorithm is used to separate out the points belonging to cracked regions based on the distribution of these point features. The proposed method was evaluated on laser-scanned point clouds from the 2015 Nepal earthquake as well as a disaster response training facility in the U.S. Evaluation results based on the point-level precision and recall metrics showed that CrackEmbed in conjunction with the isolation forest algorithm resulted in the best performance overall.

Field Information Modeling (FIM)™: Best Practices Using Point Clouds

This study presented established methods, along with new algorithmic developments, to automate point cloud processing in support of the Field Information Modeling (FIM)™ framework. More specifically, given a multi-dimensional (n-D) designed information model, and the point cloud’s spatial uncertainty, the problem of automatic assignment of point clouds to their corresponding model elements was considered. The methods addressed two classes of field conditions, namely (i) negligible construction errors and (ii) the existence of construction errors. Emphasis was given to defining the assumptions, potentials, and limitations of each method in practical settings. Considering the shortcomings of current frameworks, three generic algorithms were designed to address the point-cloud-to-model assignment. The algorithms include new developments for (i) point cloud vs. model comparison (negligible construction errors), (ii) robust point neighborhood definition, and (iii) Monte-Carlo-based point-cloud-to-model surface hypothesis testing (existence of construction errors). The effectiveness of the new methods was demonstrated in real-world point clouds, acquired from construction projects, with promising results. For the overall problem of point-cloud-to-model assignment, the proposed point cloud vs. model and point-cloud-to-model hypothesis testing methods achieved F-measures of 99.3% and 98.4%, respectively, on real-world datasets.

Conditional dependencies in imprecise data handling

Experimental data are meant as instances of random variables. Stochastic distortions are empirically evaluated. Evaluations take form of histograms. Set of rectangle bins of a histogram can be converted to Gaussian like curve enabling exploration of a discernment point neighborhood. Transformation can be made using fuzzy systems concept. Empirical distributions at hand differ. Discrepancies introduce some kind of doubtfulness or ambiguity. Many modern applications embrace uncertainty as an important factor. New solutions include alternative approach towards modelling and processing uncertainty. Mathematical Theory of Evidence is useful and provides supporting measures on exploration of the conditional relationship that requires penetration of neighborhood of a random variable instance. In the paper transformations of probability density distributions using fuzzy sets are proposed. The aim of suggested conversion is to deliver locally injective function to obtain conditional relationships measures.

Analyzing New York City 311 Call Center Data related to Hurricane Sandy

Background: Hurricane Sandy made landfall on October 29 2012 and caused immediate health and economic impacts in the greater New York City (NYC) area. However, the long-term consequences of the storm are not fully understood. We analyzed NYC 311 data to quantify the extent of Hurricane Sandy related concerns in NYC. Methods: Data from NYC 311 Call Center Inquiry and Service Requests were downloaded from the NYC Open Data website (9/1/2010-3/20/20). We included only calls containing the keyword: “Sandy”. Descriptive analyses were conducted to understand the characteristics of the 311 calls, including the date, agency contacted, the geographic location of the caller if available, a brief description and the resolution of each call. Results: Eighty percent of the 78,541 calls specific to Hurricane Sandy were made in 2012, representing 2.1% of all 311 calls in 2012. The top three contacted agencies were 311 Call Center (36.1%), Department of Education (16.2%), and City Hall (13.6%). The main call-in reasons were to obtain information about the City's response to Hurricane Sandy (36.0%), public school openings/closings related to Sandy (14.9%), and locations of walk-in centers offering assistance with recovery services (11.2%). There were 231 service requests made between 9/1/2012 and 12/31/2017 about destroyed buildings due to Sandy, of which 95.9% occurred in 2013. Calls were from Queens (83%), Staten Island (15%) and Brooklyn (2%), with the majority occurring in the Breezy Point neighborhood of Queens (62.6%). Conclusion: The 311 call center plays an important role in receiving and disseminating information about hurricane preparedness and recovery/relief in both the immediate and long-term aftermath of Hurricane Sandy. Evidenced by Hurricane Sandy calls persisting into 2020, ongoing efforts must be made to enhance disaster preparedness and resilience in communities, in addition to immediately responding to needs of communities after natural disasters.

A Note from the Editor

Previous articleNext article FreeA Note from the EditorSuzanna Danuta WaltersSuzanna Danuta WaltersNortheastern University Search for more articles by this author PDFPDF PLUSFull Text Add to favoritesDownload CitationTrack CitationsPermissionsReprints Share onFacebookTwitterLinked InRedditEmailQR Code SectionsMoreLeo Tolstoy may be right that “All happy families are alike; each unhappy family is unhappy in its own way,” but feminist scholars know that whatever their varying levels of happiness, family remains a fraught place, so often where the seeds of sexist inequity and gender normativity are planted. But familial roles—performed differently and imagined otherwise—can also be sites for transformative self making, political engagement, and creative resource management, as Brady G’sell articulates in her analysis of the “multiple maternities” of poor women in the Point neighborhood of Durban, South Africa. Family and home crop up in another manner in Leanne Trapedo Sims’s article on prison writing in Hawai‘i, where the space of home is “both wound and desire” (224) for incarcerated women, refracted through the lies and violence of domesticity and the realities of reconfigured attachments.Reconfigured attachments also appear in this issue through some robustly queer and queerly feminist scholarship. I admit I’ve been a bit disappointed that we haven’t received more queer work, especially queer scholarship working within a feminist analytic. So it is exciting indeed to include the pieces by Hélène Frohard-Dourlent et al., Carla Rice et al., and Anahi Russo Garrido, all of which engage with the complicated—and often contradictory—ways in which people live and name their sexual practices and gender identities. From the creatively queer and polyamorous relationships in post–same sex marriage Mexico City that engage radically with existing state traditions even as new names are invented; to the trans youth of Canada who employ both essentialist and social-constructionist renderings of identity in an attempt to make, as Judith Butler would have it, livable lives; to the queer women participants who reimagine body politics in a video project, these contributions are all testaments to the determined ways in which feminists mold self and collectivity in the face of intractable odds. Questions of representation also crop up in Jessica Ringrose and Kaitlyn Regehr’s methodologically innovative piece on the varied responses to racialized and sexist advertising throughout the London transport system.And speaking of intractable odds, it’s hard to overstate the persistence of sexual violence in women’s lives, and our scholarship reflects this harsh reality. Nary a month goes by without numerous submissions from across the globe analyzing and contesting and bemoaning. In this issue, Rupal Oza examines how complicated the question of false narratives can be in a context in which female sexual agency is always already suspect and how hegemonic discourses of “false cases” attempt to undermine all women’s credibility, just as some attention is finally beginning to be paid to the prevalence of sexual violence in Indian women’s lives. And turning to the United States, Rosanne Kennedy and Hannah McCann examine feminist responses to campus sexual assault through a broad and generous/critical engagement with the work of legal scholar Janet Halley in conjunction with the controversial film The Hunting Ground.In this same tradition of thoughtful interrogation of our own intellectual production, Sami Schalk and Jina Kim ask “what would feminist disability studies look like if it were grounded in feminist-of-color theory?” (31) and provide a rich framework that adds much to contemporary theorizing. And last, but certainly not least, Lindsey Stewart’s (re)investigation of Zora Neale Hurston’s complicated and contested racial politics as refracted through Beyoncé’s epic visual album Lemonade exemplifies the kind of creative interdisciplinary scholarship that Signs has always championed: bold, innovative, edgy. Once again, a treasure trove of feminist scholarship from Signs: enjoy! Previous articleNext article DetailsFiguresReferencesCited by Signs Volume 46, Number 1Autumn 2020 Article DOIhttps://doi.org/10.1086/709253 © 2020 by The University of Chicago. All rights reserved.PDF download Crossref reports no articles citing this article.

CLASSIFICATION OF RAILWAY ASSETS IN MOBILE MAPPING POINT CLOUDS

Abstract. Nowadays, mobile mapping systems are widely used to quickly collect reliable geospatial information of relatively large areas: thanks to such characteristics, the number of applications and fields exploiting their usage is continuously increasing. Among such possible applications, mobile mapping systems have been recently considered also by railway system managers to quickly produce and update a database of the geospatial features of such system, also called assets. Despite several vehicles, devices and acquisition methods can be considered for the data collection of the railway system, the predominant one is probably that based on the use of a mobile mapping system mounted on a train, which moves all along the railway tracks, enabling the 3D reproduction of the entire railway track area.Given the large amount of data collected by such mobile mapping, automatic procedures have to be used to speed up the process of extracting the spatial information of interest, i.e. assets positions and characteristics.This paper considers the problem of extracting such information for what concerns cantilever and portal masts, by exploiting a mixed approach. First, a set of candidate areas are extracted and pre-processed by considering certain of their geometric characteristics, mainly extracted by using eigenvalues of the covariance matrix of a point neighborhood. Then, a 3D modified Fisher vector-deep learning neural net is used to classify the candidates. Tests on such approach are conducted in two areas of the Italian railway system.

Local Peaks-Based Clustering Algorithm in Symmetric Neighborhood Graph

Density-based clustering methods have achieved many applications in data mining, whereas most of them still likely suffer poor performances on data sets with extremely uneven distributions, like the manifold or ring data. The paper proposes a novel method for clustering with local peaks in the symmetric neighborhood. Local peaks are points with maximum densities at the local level. During the searching of local peaks, all data, except those outliers, can be easily divided into a number of small clusters in accordance with the local peaks in each point's neighborhood. Especially, a graph-based scheme is adopted here to merge similar clusters based on their similarity in the symmetric neighborhood graph, followed by assigning each outlier to the closest cluster. A variety of artificial, real data sets and a real building data set have been tested for clustering by the proposed method and compared against other popular density-based methods and other algorithms.

Efficient extraction algorithm for local fuzzy features of dynamic images

Aiming at the poor extraction effect of the current extraction algorithm for local fuzzy features of dynamic images and the low extraction accuracy, a new algorithm based on FAST corner is proposed to extract the local fuzzy feature of dynamic images efficiently. Through analyzing the mode distortion existing in the local fuzzy features of dynamic images, and processing the spatial domain of dynamic images by using point processing and neighborhood processing, and processing the image frequency domain by filtering, the preprocessing of dynamic images and the effect of local fuzzy feature extraction of dynamic images are improved. On the basis of this, aiming at the shortcomings of FAST corner extraction of local fuzzy features of dynamic images, this paper puts forward the idea of algorithm optimization, and analyzes the realization process of the improved algorithm to achieve the algorithm optimization processing and complete the local fuzzy feature extraction of dynamic images. Based on the least squares method, the inaccurate local fuzzy features in the dynamic images are removed to ensure the accuracy of feature extraction. Experimental results show that the proposed algorithm can accurately extract the local fuzzy features of dynamic images, and the extraction results are better.

A Point-to-Ray Framework for Generating Smooth Parallel Parking Maneuvers

The problem of generating smooth parallel parking maneuver for autonomous car-like vehicles is considered, wherein the vehicle moves forward from the starting point to an intermediate point and then traces a path backward to the parking position. Any error in accurately reaching the intermediate point leads to maneuver replanning and hence puts extra computational burden on the planner. Addressing the issue, this letter formulates the problem in a point-to-ray framework connecting the parking position to all points in a neighborhood of the intermediate point. Exploiting its asymptotic convergence, four parameter logistic (4PL) curve based S-shaped path is proposed as a solution with a single maneuver connecting the intermediate point neighborhood to the parking position. Inherent to the 4PL curve, the maneuver presents a continuous steering profile with zero steering angle at the ends. Parking slot collision avoidance and steering limit of the vehicle are satisfied by deducing closed-form analytic conditions on the two design parameters. Together with the robustness to intermediate point variation, comparison with existing approaches highlights computational and design advantages. The proposed approach is validated using a double-tracked Ackermann steering vehicle model.

Analyzing investment flows in comprehensive community revitalization: The case of Bayview Hunters Point, San Francisco

ABSTRACTDespite the rise of comprehensive community initiatives, little research evaluates the specific type and extent of neighborhood investment generated by government spending. This study uses the case of housing revitalization in San Francisco’s Bayview Hunters Point neighborhood to determine the feasibility of analyzing public, private, and nonprofit investment flows related to the Choice Neighborhoods Initiative and HOPE SF. Through analyses of both secondary and primary data, this article finds that not only is identifying all of the different investments challenging but that multiple factors complicate analysis, including the timing of the business cycle, city budget cutbacks, market pressures from surrounding neighborhoods, and real estate speculation. Furthermore, neighborhood residents are perceiving little change—or at least few benefits for themselves—in the area. This suggests the need for different tools, such as value capture strategies and community benefits agreements, to better serve the needs of existing residents of low-income communities.

Framework to trade optimality for local processing in large-scale wavefront reconstruction problems.

We show that the minimum variance wavefront estimation problems permit localized approximate solutions, in the sense that the wavefront value at a point (excluding unobservable modes, such as the piston mode) can be approximated by a linear combination of the wavefront slope measurements in the point's neighborhood. This enables us to efficiently compute a wavefront estimate by performing a single sparse matrix-vector multiplication. Moreover, our results open the possibility for the development of wavefront estimators that can be easily implemented in a decentralized/distributed manner, and in which the estimate optimality can be easily traded for computational efficiency. We numerically validate our approach on Hudgin wavefront sensor geometries, and the results can be easily generalized to Fried geometries.

A Crouching Village: Ebola and the Empty Gestures of Quarantine in Monrovia

AbstractIn its scale, its virulence, and, most significantly, its urbanity, the 2014‐2015 outbreak of Ebola Virus Disease (EVD or simply Ebola) in the Mano River region of West Africa seemed to challenge the basic tools of modern public health. As a result, the government of Liberia took the extraordinary step of implementing a cordon sanitaire around one of the poorest neighborhoods in the capital city, Monrovia. Observers labeled both the quarantine and the ensuing riots as barbaric acts not seen since the plagues of the Middle Ages. This characterization masked the real history of racial separation that defines many African cities, and cast Monrovia's poorest residents as morally bankrupt, primitive savages. In this article I instead explore the quarantine as an instance of the repetition of a familiar urban form, and read the ensuing violence in Monrovia's West Point neighborhood not as an ethical problem but as an “empty” political gesture. [Liberia; Ebola; Quarantine; Urban Planning; Urban Violence; Monrovia]

Spatio-Temporal Point Path Analysis and Optimization of a Galvanoscopic Scanning Laser Projector.

Galvanoscopic scanning laser projectors are powerful vector graphic devices offering a tremendous local brightness advantage compared to standard video projection systems. However, such devices have inherent problems, such as temporal flicker and spatially inaccurate rendering. We propose a method to generate an accurate point-based projection with such devices. To overcome the mentioned problems, we present a camera-based method to automatically analyze the laser projector's motion behavior. With this information, a model database is generated that is used to optimize the scanning path of projected point sequences. The optimization considers the overall path length, its angular shape, acceleration behavior, and the spatio-temporal point neighborhood. The method minimizes perceived visual flickering while guaranteeing an accurate spatial point projection at the same time. Comparisons and timing measurements prove the effectiveness of our method. An informal user evaluation shows substantial visual quality improvement as well.

A new test statistic for climate models that includes field and spatial dependencies using Gaussian Markov random fields

Abstract. A new test statistic for climate model evaluation has been developed that potentially mitigates some of the limitations that exist for observing and representing field and space dependencies of climate phenomena. Traditionally such dependencies have been ignored when climate models have been evaluated against observational data, which makes it difficult to assess whether any given model is simulating observed climate for the right reasons. The new statistic uses Gaussian Markov random fields for estimating field and space dependencies within a first-order grid point neighborhood structure. We illustrate the ability of Gaussian Markov random fields to represent empirical estimates of field and space covariances using "witch hat" graphs. We further use the new statistic to evaluate the tropical response of a climate model (CAM3.1) to changes in two parameters important to its representation of cloud and precipitation physics. Overall, the inclusion of dependency information did not alter significantly the recognition of those regions of parameter space that best approximated observations. However, there were some qualitative differences in the shape of the response surface that suggest how such a measure could affect estimates of model uncertainty.