Map Representation Research Articles

An Analytical Elastic Solution for Right-Angle Trapezoidal Opening in Steeply Inclined Coal Seam

In the process of underground mining, steeply inclined rocks or coal seams are often encountered, forming the openings of right-angle trapezoid. According to the geological conditions of a mining project in China, an analytical elastic solution of stress and displacement around right-angle trapezoidal opening in a homogeneous, isotropic, and linear elastic geomaterial is presented, which is based on the evaluation of the conformal mapping representation by an appropriate numerical calculation and the complex potential functions. The different results from other shaped openings are shown as follows. In a right-angle trapezoidal opening, the maximum displacements of roof falling occur on the low side, while the most horizontal displacements on the low side are around the roof and the most horizontal displacements on the high side are around the middle of the high side in this opening. These results are also compared with the numerical calculations in FLAC software, illustrating that the solution may be easily applied to rock mechanics or rock engineering for understanding the deformation of floor heave and roof falling down. The solution is also suitable for optimum design of bolt supporting in a right-angle trapezoidal opening, which is different from the traditional concept of symmetrical bolt supporting. Finally, a methodology is proposed for the estimation of conformal mapping coefficients for a given cross-sectional shape of an opening without symmetrical axis.

Slam Algorithms for Different Map Representations

The paper discusses the generalized simultaneous localization and mapping problem statement from the standpoint of the Bayesian approach and its relationship with algorithms for different map representations. The two-dimensional example describes the linearized simultaneous localization and mapping algorithm for the mobile platform in two-dimensional space.

Ultralow-Power Localization of Insect-Scale Drones: Interplay of Probabilistic Filtering and Compute-in-Memory

We propose a novel compute-in-memory (CIM)-based ultralow-power framework for probabilistic localization of insect-scale drones. Localization is a critical subroutine for path planning and rotor control in drones, where a drone is required to continuously estimate its pose (position and orientation) in flying space. The conventional probabilistic localization approaches rely on the 3-D Gaussian mixture model (GMM)-based representation of a 3-D map. A GMM model with hundreds of mixture functions is typically needed to adequately learn and represent the intricacies of the map. Meanwhile, localization using complex GMM map models is computationally intensive. Since insect-scale drones operate under extremely limited area/power budget, continuous localization using GMM models entails much higher operating energy, thereby limiting flying duration and/or size of the drone due to a larger battery. Addressing the computational challenges of localization in an insect-scale drone using a CIM approach, we propose a novel framework of 3-D map representation using a harmonic mean of the “Gaussian-like” mixture (HMGM) model. We show that <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">short-circuit current</i> of a multiinput floating-gate CMOS-based inverter follows the harmonic mean of a Gaussian-like function. Therefore, the likelihood function useful for drone localization can be efficiently implemented by connecting many multiinput inverters in parallel, each programmed with the parameters of the 3-D map model represented as HMGM. When the depth measurements are projected to the input of the implementation, the summed current of the inverters emulates the likelihood of the measurement. We have characterized our approach on an RGB-D scenes dataset. The proposed localization framework is <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\sim 25\times $ </tex-math></inline-formula> energy-efficient than the traditional, 8-bit digital GMM-based processor paving the way for tiny autonomous drones.

Comparative Evaluation of Bipartite, Node-Link, and Matrix-Based Network Representations.

This work investigates and compares the performance of node-link diagrams, adjacency matrices, and bipartite layouts for visualizing networks. In a crowd-sourced user study ( n=150), we measure the task accuracy and completion time of the three representations for different network classes and properties. In contrast to the literature, which covers mostly topology-based tasks (e.g., path finding) in small datasets, we mainly focus on overview tasks for large and directed networks. We consider three overview tasks on networks with 500 nodes: (T1) network class identification, (T2) cluster detection, and (T3) network density estimation, and two detailed tasks: (T4) node in-degree vs. out-degree and (T5) representation mapping, on networks with 50 and 20 nodes, respectively. Our results show that bipartite layouts are beneficial for revealing the overall network structure, while adjacency matrices are most reliable across the different tasks.

Multimodal Information Fusion Approach for Noncontact Heart Rate Estimation Using Facial Videos and Graph Convolutional Network

Heart rate (HR) is a critical signal for reflecting human physical and mental conditions, and it is beneficial for diagnosing neurological and cardiovascular diseases due to its excellent accessibility. However, traditional HR measurement devices have limited usability and convenience. Recent studies have shown that the optical absorption variation of human skin due to blood volume variation in cardiac cycles can be acquired from facial videos and used to estimate HR in a non-contact manner. However, the advanced non-contact HR estimation approaches are based on a single HR information source, resulting in unsatisfactory estimation results due to noise corruption and insufficient information. To address these problems, this paper proposes a multimodal information fusion framework for non-contact HR estimation. First, feature representation maps are used to effectively extract periodic signals from facial visible-light and thermal infrared videos. Then, a temporal-information-aware HR feature extraction network (THR-Net) for encoding discriminative spatiotemporal information from the representation maps is presented. Finally, based on a graph convolution network (GCN), an information fusion model is proposed for feature integration and HR estimation. Experimental and evaluation results of five different metrics on two datasets show that the proposed approach outperforms the state-of-the-art approaches. This paper demonstrates the advantage of multimodal information fusion for non-contact HR estimation.

Road Extraction From Remote Sensing Images in Wildland–Urban Interface Areas

In this letter, we address the problem of road extraction in Wildland–urban interface (WUI) areas. In recent years, with the great success of convolutional neural networks (CNNs) in various vision-related tasks, researchers have developed many CNN-based methods for road extraction on remote sensing images. Nevertheless, these methods mostly treat road extraction as a binary classification problem on semantic labeling. In WUI areas, the road is narrower and tends to be occluded by trees, which may result in the serious discontinuous problem of inferred road maps. To address this issue, we propose transforming the input representation of the binary classification map into a continuous signed distance map. In this way, our model is forced to predict the continuous distance representations and, thus, improve the spatial continuities of inferred roads. In addition, a real-value regression task is designed to train along with the original binary classification task to generate spatially continuous and semantically accurate road maps. Then, we conduct experiments on the public Massachusetts road data set and a homemade data set collected from Yajishan Mountain, Beijing, China. Finally, our proposed method achieves intersection-over-unions (IoUs) of 64.11% and 65.92% for the Massachusetts and WUI-Yajishan data sets, respectively, without any postprocessing. In addition, the ablation analysis shows that introducing the regression task on the proposed signed distance representation can effectively alleviate the problem of discontinuous road prediction. Furthermore, comparing with the state-of-the-art methods demonstrates the superiority of our method for road extraction in WUI areas.

Adversarial Attack Using Sparse Representation of Feature Maps

Deep neural networks can be fooled by small imperceptible perturbations called adversarial examples. Although these examples are carefully crafted, they involve two major concerns. In some cases, adversarial examples generated are much larger than minimal adversarial perturbations while in others the attack method involves an extensive number of iterations making it infeasible. Moreover, the sparse attacks are either too complex or are not sparse enough to achieve imperceptibility. Therefore, attacks designed should be fast and minimum in terms of ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> -norm. In this research, we used a dictionary learning technique to generate sparse adversarial examples based on feature maps of target images. We present two novel algorithms to tune the dictionary learning process and feature map selection. The results on MNIST and Imagenet show our attack is better or competitive with the state-of-the-art methods. We also compared our method with sparse attacks recently introduced in literature. As a result, we have achieved comparable attack success rate when compared to the state-of-the-art with smaller ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> -norm. We also tested the efficacy of our attack in the presence of defense mechanisms and none of the defenses were able to combat the effect of our proposed attack.

Comparing Visual Representations of Collaborative Map Interfaces for Immersive Virtual Environments

Comparing Visual Representations of Collaborative Map Interfaces for Immersive Virtual Environments

The potency of architectural probabilism in shaping cognitive environments: A psychophysical approach

The article deals with a controversial issue, namely the effect of architecture and the built environment on human behaviour. Two debatable approaches dominated the architectural field in the past decades: determinism and possibilism. Both have proved inadequate in explaining human-environment relationships. Probabilism emerged as a tradeoff, providing an interface that accentuated the role of architecture in influencing human behaviour. The article presents a theoretical framework to explore the concept of probabilism, and the extent to which architectural intervention can affect the perceptual and cognitive behaviour of individuals. A psychophysical study is conducted on a selected environment (University of Baghdad campus) based on two approaches: the cognitive representation and mental mapping of individuals (the graphic recall), and the perceptual selectivity of environmental elements (the verbal recall). The findings assert the significant impact of the physical aspects (semblance and positioning attributes) of environmental stimuli, thereby accentuating the probabilistic approach to human behaviour.

Analysis of the Impact of the Development Level of Aerobics Movement on the Public Health of the Whole Population Based on Artificial Intelligence Technology.

With the enhancement of China's comprehensive national power and the improvement of people's living standards, health has become the goal that people pursue. While people are thirsty for extensive knowledge and a healthy body, they also pay more attention to the cultivation of elegant temperament and the enjoyment of beauty, and aerobics has become a hot spot for national fitness with its advantages of coordinated and beautiful movements, bright and cheerful rhythm and obvious fitness effects. Aerobics is a new popular fitness sports, from the beginning of development by most fitness enthusiasts, especially it is a women's favorite. To this end, the characteristics, value, status, and role of aerobics in the public health of all people are discussed, and the problems of poor recognition effect in the existing aerobics difficulty aerobics action recognition methods are proposed to apply the graph convolutional neural network to the aerobics difficulty aerobics action recognition. The video of aerobics is divided into several images, and the background of the aerobics difficult aerobics action image is eliminated, and the gray scale co-generation matrix is set to estimate the local area blur kernel of the difficult action image to correct the visual error of the difficult action image. “change to” The aerobics action is divided into several difficult action images, and the gray-scale symbiosis matrix is set to estimate the local area fuzzy core of the difficult action image, and correct the visual error of the difficult action image. On this basis, the graph convolutional neural network is pre-trained to construct a human-directed spatial-temporal skeleton map, and the human-directed spatial-temporal map representation is modeled with temporal dynamic information to achieve aerobics difficult aerobics action recognition. The experimental results show that the recognition time of the difficult aerobics movements based on the graph convolutional neural network is shorter and the number of false recognitions is less in complex and simple backgrounds, which proves that the proposed method improves the recognition of difficult aerobics movements to achieve the goal of promoting the development level of aerobics and improving the public health of all people.

Identification and Analysis of Glass Components by Fusing K-Means Clustering and Ridge Regression

The craft production of glass has a long history, and glass has been a valuable physical evidence of trade exchanges during the Silk Road. As traditional value crafts, the study and identification of the chemical composition of glass is of great significance. This paper mainly completes the classification problem by K-means clustering analysis and clustering tree, etc.; the statistical law study and prediction analysis of data are completed by integrating radial basis ridge regression and ARIMA differential autoregression, etc., which solves the problem of identification, classification and prediction of glass components. Firstly, the correlation analysis of the given data is carried out, and the Pearson correlation coefficient is calculated and visualized as the heat map representation, and performed statistical law fitting analysis of the data by ridge regression with fused radial bases, and finally predicted the chemical composition content before and after weathering by Autoregressive Integrated Moving Average (ARIMA) differential autoregression.

Deep Learning-Enabled High-Resolution and Fast Sound Source Localization in Spherical Microphone Array System

While sound source localization (SSL) using a spherical microphone array system can be applied to obtain visual beam patterns of source distribution maps in a range of omnidirectional acoustic applications, the present challenges of the spherical measurement system on the valid frequency ranges and the spatial distortion as well as the grid-related limitations of data-driven SSL approaches raise the need to develop an appropriate method. Imbued by these challenges, this study proposes a deep learning approach to achieve the high-resolution performance of localizing multiple sound sources tailored for omnidirectional acoustic applications. First, we present a spherical target map representation that can panoramically pinpoint the position and strength information of multiple sound sources without any grid-related constraints. Then, a dual-branched spherical convolutional autoencoder is proposed to obtain high-resolution localization results from the conventional spherical beamforming maps while incorporating frequency-variant and distortion-invariant strategies to address the inherent challenges. We quantitatively and qualitatively assess our proposed method’s localization capability for multiple sound sources and validate that the proposed method can achieve far more precise and computationally-efficient results than the existing approaches. By extension, we newly present the experimental setup that can create omnidirectional acoustic scenarios for the multiple sound source localization. By evaluating our proposed method in this experimental setup, we demonstrate the effectiveness and applicability of the proposed method with the experimental data. Our study delivers the proposed approach’s potential of being utilized in various sound source localization applications.

Remote Sensing Fine-Grained Ship Data Augmentation Pipeline With Local-Aware Progressive Image-to-Image Translation

Remote sensing image ship fine-grained classification is a challenging vision problem due to factors such as inter-class similarity, real-world image scarcity, and class imbalance. Data augmentation aims to solve these problems from the data perspective. Most of these methods cannot work effectively in complex scenes, which restricts their practical application. Here, we propose a novel data augmentation pipeline based on local-aware image translation to achieve the representation mapping between cross-domain corresponding instances. Our pipeline contains three modules: Imaging Simulation System (ISS), Local-aware Progressive Image-to-Image Translation (LoPIT), and Image Harmonization (IH) modules. The ISS module generates simulated images with correct appearance and diverse features based on the input requirement information. To tackle the domain gap between simulated image and real-world image, we propose the Local-aware CycleGAN in the LoPIT module to achieve mapping based on local-aware learning and apply two sub-modules to progressively complete the remote sensing image global cross-domain translation. The IH module uses image harmonization technology to coordinate the visual appearance between the foreground and background to generate sufficient remote sensing ship images with precise representation, photorealistic style, and harmonious features. Moreover, we present a mixed dataset including real-world images and our synthetic images for remote sensing image fine-grained ship classification. Our dataset named RSSA-12 contains 12 categories of ship targets in 3831 images with high-quality annotated category labels, effectively alleviating the long-tail problem of existing datasets. Experimental results demonstrate that our progressive pipeline outperforms the state-of-the-art data augmentation method on the remote sensing fine-grained ship classification task.

Strength and settlement of subgrade soil in southern part of Kangeyam block

Subgrade must typically be capable of supporting weights transmitted from the pavement or building structure. The relative compaction, optimum moisture content, and type of soil everything have an influence on bearing capacity. A good subgrade can withstand a large amount of loading without an excessive deformation. When subjected to heavy moisture or freezing temperatures, most soils experience some volume change. In case of pavement, highway pavements eventually rest on native soil, the subgrade serves as the foundation material for the pavement construction (subgrade). As a result, the features of subgrade have an impact on the performance of pavement. It is therefore critical for an engineer to comprehend the precise nature of the relationship between subgrade strength and settlement characteristics due tomoisture fluctuation. This paper represents that the soil sample taken in and around southern part of Kangeyam block (Kangeyam, Ganapathipalayam, Alangadu, Sampanthanpalayam, Sivanmalai, Periyailliyam, Padiyur, Veeranaampalayam, Vattamalai, Aratholuvu) was tested and experimented for its index and engineering properties. The index properties test includes specific gravity, sieve analysis, OMC (optimum moisture content) and MDD (maximum dry density) was conducted for classification of soil. Since the soil samples collected was predominantly identified sand of various gradation, the engineering properties test such as direct shear test was conducted to evaluate their shear strength properties. The subgrade strength of the soil samples was estimated by conducting CBR (California Bearing Ratio) test. In addition, the settlement of soil samples was evaluated as flexible pavement. The results of the tests data were entered in GIS (Geographical Information System) to describe the data as spatial representation mapping for the zone. This will ensure that the numerous subgrade qualities found throughout the zone are evaluated for safety and referred for future approaches.

Inhomogeneous Conformal Field Theory Out of Equilibrium

We study the non-equilibrium dynamics of conformal field theory (CFT) in 1+1 dimensions with a smooth position-dependent velocity v(x) explicitly breaking translation invariance. Such inhomogeneous CFT is argued to effectively describe 1+1-dimensional quantum many-body systems with certain inhomogeneities varying on mesoscopic scales. Both heat and charge transport are studied, where, for concreteness, we suppose that our CFT has a conserved U(1) current. Based on projective unitary representations of diffeomorphisms and smooth maps in Minkowskian CFT, we obtain a recipe for computing the exact non-equilibrium dynamics in inhomogeneous CFT when evolving from initial states defined by smooth inverse-temperature and chemical-potential profiles β(x)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\beta (x)$$\\end{document} and μ(x)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\mu (x)$$\\end{document}. Using this recipe, the following exact analytical results are obtained: (i) the full time evolution of densities and currents for heat and charge transport, (ii) correlation functions for components of the energy–momentum tensor and the U(1) current as well as for any primary field, and (iii) the thermal and electrical conductivities. The latter are computed by direct dynamical considerations and alternatively using a Green–Kubo formula. Both give the same explicit expressions for the conductivities, which reveal how inhomogeneous dynamics opens up the possibility for diffusion as well as implies a generalization of the Wiedemann–Franz law to finite times within CFT.

Thermography and Orthodontics

Thermography (or thermal infrared imaging - IR imaging) is the graphic representation of the body temperature map obtained by the non contact and non invasive recording of the spontaneous infrared thermal emission of the body at the superficial level of the human body

REVISITING THE CONCEPT OF THE JAVANESE ISLAM: GENEALOGY, ACADEMIC REPRESENTATION, AND CULTURAL STRATEGY

This article aims to revisit the understanding of the concept of syncretic Javanese Islam, which scholars have much debated. From the genealogical search for the syncretic Javanese Islamic concept produced by missionaries and orientalists; mapping of the academic representations of Javanese Islam produced by recent scholars; up to an explanation of the theoretical approach used to discuss the concept of syncretism. Based on selected literature sources, the results show that the syncretic Javanese Islamic concept formulated by missionaries and orientalists tends to be highly theological due to the Christian framework used. They were especially concerned about the universality of religion, the evolution of civilization, the hierarchy of religions, and the purity of religious doctrine. Recent scholars also tend to have inherited this conception of Javanese Islam. They constantly debated the ‘true’ or the ‘purity’ of Javanese Islam, thus resulting in the two dominantly opposing views. The intensity of the cultural encounter and the necessity of syncretism requires a more robust theoretical approach to obtain a more precise understanding of syncretism.

Comparing Map Learning between Touchscreen-Based Visual and Haptic Displays: A Behavioral Evaluation with Blind and Sighted Users

The ubiquity of multimodal smart devices affords new opportunities for eyes-free applications for conveying graphical information to both sighted and visually impaired users. Using previously established haptic design guidelines for generic rendering of graphical content on touchscreen interfaces, the current study evaluates the learning and mental representation of digital maps, representing a key real-world translational eyes-free application. Two experiments involving 12 blind participants and 16 sighted participants compared cognitive map development and test performance on a range of spatio-behavioral tasks across three information-matched learning-mode conditions: (1) our prototype vibro-audio map (VAM), (2) traditional hardcopy-tactile maps, and (3) visual maps. Results demonstrated that when perceptual parameters of the stimuli were matched between modalities during haptic and visual map learning, test performance was highly similar (functionally equivalent) between the learning modes and participant groups. These results suggest equivalent cognitive map formation between both blind and sighted users and between maps learned from different sensory inputs, providing compelling evidence supporting the development of amodal spatial representations in the brain. The practical implications of these results include empirical evidence supporting a growing interest in the efficacy of multisensory interfaces as a primary interaction style for people both with and without vision. Findings challenge the long-held assumption that blind people exhibit deficits on global spatial tasks compared to their sighted peers, with results also providing empirical support for the methodological use of sighted participants in studies pertaining to technologies primarily aimed at supporting blind users.

Sensorimotor Self-organization via Circular-Reactions.

Newborns demonstrate innate abilities in coordinating their sensory and motor systems through reflexes. One notable characteristic is circular reactions consisting of self-generated motor actions that lead to correlated sensory and motor activities. This paper describes a model for goal-directed reaching based on circular reactions and exocentric reference-frames. The model is built using physiologically plausible visual processing modules and arm-control neural networks. The model incorporates map representations with ego- and exo-centric reference frames for sensory inputs, vector representations for motor systems, as well as local associative learning that result from arm explorations. The integration of these modules is simulated and tested in a three-dimensional spatial environment using Unity3D. The results show that, through self-generated activities, the model self-organizes to generate accurate arm movements that are tolerant with respect to various sources of noise.

Austrianvineyards.com – the world’s first nationwide information and presentation system of all wine estates

Abstract. The origin of wine is of crucial importance in the wine industry. It is an essential part of quality and is generally closely associated with the terroir of a wine. The smaller/specific the origin, the higher the attributed quality. Austria is the first country to summarise all its wine origins from the national level down to the vineyard level in a uniform way and make them available.Austrianvineyards.com is a project that is conceptually designed and developed by the University of Vienna, Department of Geography and Regional Research in cooperation with the plan+land agency. As part of an applied research and development project commissioned by the Austrian Wine Marketing Board (ÖWM), the data on all Austrian wine estates were standardised and then cartographically visualised.The project has a high scientific development component. In the sense of a scientifically qualitative and sustainable implementation of the project goals, research focal points were defined within the framework of the project. These included topics of efficient data management of wine-relevant (geo) information, development of adapted cartographic visualisation methods as well as concepts for user-oriented communication and presentation of the contents.The implementation should do justice to the "image of excellence" of the Austrian wine industry and be considered "state of the art" worldwide. Austrianvineyards.com is the first platform to offer digital (geo) information on nationwide vineyards based on legal cadastral data.Austrianvineyards.com builds on the three pillars of geo-design, geo-standardisation and geo-communication, aiming at an intuitive and comparable presentation through user-optimised and aesthetically pleasing map representations in the field of UI and UX design. In addition to the digital format, new printed vineyard maps of all Austrian wine-growing regions were cartographically designed and implemented.