Distance Information Research Articles

Hyperspectral endmember extraction using convexity based purity index

The endmember extraction is a challenging problem in spectral unmixing (SU) of a mixed pixel in hyperspectral imagery. There are plenty of attempts to solve the endmember extraction problem. Still, the pure pixel assumption-based algorithms have probably been used most in solving the endmember extraction of SU due to the light computational burden. These pure pixel assumption-based algorithms usually follow one of the criteria: (1) Maximum simplex volume or (2) Extreme projection on a subspace. We propose a novel integrated framework that uses both the criteria mentioned above and the proposed one is referred to as the Convexity-based Pure Index (CPI) algorithm. The CPI generates a fixed number of convex sets based on the number of available bands in the hyperspectral image. The algorithm defines the purity score based on the availability of pixels in the convex sets for the two-band data. The CPI has been compared with contemporary algorithms such as Automatic Target Generation Process (ATGP), Vertex Component Analysis (VCA), Pixel Purity Index (PPI), Successive Volume MAXimization (SVMAX), Alternating Volume MAXimization (AVMAX), TRIple-P: P-norm based Pure Pixel identification (TRIP), Successive Decoupled Volume Max–Min (SDVMM), Negative ABundance-Oriented (NABO), and Entropy-based Convex Set Optimization (ECSO). The metrics, Spectral Angle Distance (SAD) and Spectral Information Divergence (SID) used in the comparison were improved up to 5.9% and 9%, respectively. The CPI outperforms prevailing algorithms on real benchmark data and new AVIRIS-NG data. The robustness of the CPI is also tested for various noisy synthetic data. The efficacy of the proposed algorithm is also tested by using qualitative analysis by visualizing the spectra comparison, and abundance maps for all real data.

DoseDiff: Distance-Aware Diffusion Model for Dose Prediction in Radiotherapy.

Treatment planning, which is a critical component of the radiotherapy workflow, is typically carried out by a medical physicist in a time-consuming trial-and-error manner. Previous studies have proposed knowledge-based or deep-learning-based methods for predicting dose distribution maps to assist medical physicists in improving the efficiency of treatment planning. However, these dose prediction methods usually fail to effectively utilize distance information between surrounding tissues and targets or organs-at-risk (OARs). Moreover, they are poor at maintaining the distribution characteristics of ray paths in the predicted dose distribution maps, resulting in a loss of valuable information. In this paper, we propose a distance-aware diffusion model (DoseDiff) for precise prediction of dose distribution. We define dose prediction as a sequence of denoising steps, wherein the predicted dose distribution map is generated with the conditions of the computed tomography (CT) image and signed distance maps (SDMs). The SDMs are obtained by distance transformation from the masks of targets or OARs, which provide the distance from each pixel in the image to the outline of the targets or OARs. We further propose a multi-encoder and multi-scale fusion network (MMFNet) that incorporates multi-scale and transformer-based fusion modules to enhance information fusion between the CT image and SDMs at the feature level. We evaluate our model on two in-house datasets and a public dataset, respectively. The results demonstrate that our DoseDiff method outperforms state-of-the-art dose prediction methods in terms of both quantitative performance and visual quality.

Bibliometric Mapping: Research Development on the Topic of Quality Management on Google Scholar Using Vosviewer

This research aims to map the development of quality management research indexed in Google Scholar and analyze topics related to this topic. By adopting a qualitative descriptive method, it was carried on through three stages: (1) indexed journal with the help of Google Scholar PoP (Publish or Perish) software; (2) journals were saved as an RIS (Research Information System) file; and (3) bibliometric analysis was performed using VOS viewer. This search focused on articles discussing quality management topics in the period 2010–2023, with a limit on the number of documents of 900. The research results showed that the amount of research on quality management had fluctuated. The most publications occurred in the specified year range, namely 83 articles. The topics most discussed were quality management systems, performance, top management, education, total quality management, relations, and improvement, while those that were still very few were distance education, skills, conflict management, competence, social development, information management, and career adaptability. Therefore, these topics were likely to be of concern to the latest research on quality management in the future because there was still very little research on them.

Energy-efficient hierarchical cluster-based routing strategies for Internet of Nano-Things: Algorithms design and experimental evaluations

Nanodevices (NDs), which are only a few nanometers (nm) in size, need to communicate with each other to perform complex operations. In nanonetworks, this communication typically involves multiple hops, requiring efficient routing protocols. Existing protocols are not well suited for nanonetworks due to their high resource consumption and setup overhead. In this paper, we propose three novel routing protocols for nanodevices. Non-Back Flooding Routing (NBFR) and Layer-Based Flooding Routing (LBFR) aim to reduce unnecessary packet transmission by utilizing distance and layer information based on received signal power. On the other hand, Tree-Based Forwarding Routing (TBFR) is a unicast-based approach that aims to transmit the packet to the destination using the shortest and most reliable path possible through a tree structure. The performance of these proposed methods is compared to well-known methods in terms of packet transmission, energy consumption, end-to-end delay, and setup overhead. TBFR achieved a packet transmission success of 92.95% in topology with the highest density of nanorouters (NRs), while it reached up to 99.57% for fewer nanorouters. Moreover, its end-to-end delay values are much lower than those of multi-path routing protocols. It also consumed one-fifth of the energy compared to its most challenging multi-path competitor, NBFR, regarding packet transmission success. However, for dense nanosensor (NS) topologies, NBFR and LBFR achieved higher packet transmission rates of 87.04% and 86.66%, respectively. Furthermore, in addition to achieving low end-to-end delays, the energy consumption of NBFR is very close to that of TBFR. In summary, the tests show that TBFR is more suitable for communication among nanorouters due to the requirement of building the tree structure, which results in a slightly higher setup overhead. In contrast, NBFR and LBFR are more suitable for communication between nanosensors because of their simplicity and low setup overhead. But, it should be noted that NBFR requires a larger header than the other alternatives.

Linear polarization study of open clusters in the anticenter direction: Signature of the spiral arms

Aims. Our objective is to investigate the distribution of dust and associated large-scale structures of the Galaxy using optical linear polarization measurements of various open clusters located at different distances in the Galactic anticenter direction. Methods. We present R-band linear polarization observations of stars toward five open clusters: Kronberger 1, Berkeley 69, Berkeley 71, Berkeley 19, and King 8 in the anticenter direction. The polarization observations were carried out using the ARIES (Aryabhatta Research Institute of Observational Sciences) IMaging POLarimeter mounted on the 104 cm Sampurnanand telescope of ARIES, Nainital, making it the first study to target the polarization observations toward distant clusters (~6 kpc). We combined the observed polarization data with the distance information from the Gaia space telescope to infer the dust distribution along the line of sight. Results. The variation in the degree of polarization and extinction with distance reveals multiple dust layers in each cluster direction. In addition, common foreground-dust layers detected toward different cluster directions highlight global features such as spiral arms. Our results show that the dust clouds at 2 kpc toward Berkeley 69 and Berkeley 71 coincide with the Perseus arm, while the dust layer at ~4 kpc toward the distant clusters Berkeley 19 and King 8 indicates the presence of the Outer arm. The large-scale dust distribution obtained by combining our polarization results with previous polarization studies of nearby open clusters suggests that the anticenter direction is characterized by a low-extinction homogeneous dust distribution with a somewhat uniform orientation of the plane-of-sky component of the magnetic field along the line of sight. Conclusions. Our study demonstrates that polarization is useful as a tool for studying the large-scale dust distribution and structural features where kinematic distance methods are inadequate and cannot provide accurate distances to the dust clouds. The global dust distribution in the anticenter direction shows signatures of the intervening spiral arms.

A simulation study on visual preview control of vehicle magnetorheological suspension

Abstract At present, the boom in intelligent vehicles, especially automotives, has brought magnetorheological (MR hereinafter) suspensions into further focus. Generally, traditional MR suspensions work with acceleration sensors to respond with a non-negligible time delay issue due to the inductive property of MR damper (MRD hereinafter), which hinders their full performance especially at high-speed driving with a powerless “blind zone”. To address this issue, this study proposes a simulation study on visual preview control for vehicle MR suspension based on MPC (Model Predictive Control), since the road preview is able to compensate the time delay due to the future road information perceived in advance. First, a camera with a computer vision algorithm is employed to preview road information of target type, target size and distance to wheels. Besides, the error correction of radial and tangential distortions of camera lens are investigated, and the trajectory of wheels are predicted by a cycloid model because MR suspension only needs to respond to the road targets that the wheels will pass over. Further, the area in front of the vehicle is divided into a recognition zone and action zone, and the road targets within action zone (<1.5 m) are calculated more precisely for a prepared immediate response from the MR suspension. Then, the performance of a simulating vehicle MR suspension is investigated through a joint simulation by the automotive simulation software CarSim and the model-solving Simulink. The evident results show that the MR suspension with visual preview control performs the best in ride comfort (mean square values of sprung mass acceleration is 82% and 52% lower compared to a passive suspension and a MR suspension with skyhook control, respectively). Therefore, the visual preview control strategy of MR suspension is able to enhance the performance on the discrete road conditions.

How emissions and distance do influence the choice of eco-tourists?

ABSTRACT Given the growing environmental impact of tourism, understanding how information influences tourists’ sustainable choices is essential. This study focuses on the relatively under-researched area of how information on carbon emissions and travel distance affects tourists’ environmentally friendly choices at destinations across various time intervals. Hypothetical dichotomous questions were employed in three online surveys with 991 participants. T-tests were used to compare the effects of carbon emissions and distance information over eight, five, three, and one-hour intervals. The results indicate that both carbon emissions and distance information positively influence preferences for environmentally friendly behaviour. Additionally, more available time can lead to less environmentally friendly choices. These findings offer valuable insights for policymakers and tourism stakeholders aiming to foster sustainable practices in the industry.

An Improved Product Defect Detection Method Combining Centroid Distance and Textural Information

In order to solve the problems of a high mismatching rate and being easily affected by noise and gray transformation, an improved product defect detection method combining centroid distance and textural information is proposed in this paper. Based on image preprocessing, the improved fuzzy C-means clustering method is used to extract the closed contour features. Then, the contour center distance description operator is used for bidirectional matching, and a robust coarse matching contour pair is obtained. After the coarse matching contour pair is screened, the refined matching result is obtained by using the improved local binary pattern operator. Finally, by comparing whether the number of fine matching pairs is consistent with the number of template outlines, the detection of good and bad industrial products is realized, and the closed contour extraction experiment, the anti-rotation matching experiment, the anti-gray difference matching experiment, and the defect detection experiment of three different products are designed. The experimental results show that the improved product defect detection method has good performance in relation to anti-rotation transformation and anti-gray difference, the detection accuracy can reach more than 90%, and the detection time is up to 362.6 ms, which can meet the requirements of industrial real-time detection.

Multi-view distillation based on multi-modal fusion for few-shot action recognition (CLIP-MDMF)

In recent years, the field of few-shot action recognition (FSAR) has garnered significant attention. Although many methods primarily rely on mono-modal data, there is a growing trend towards utilizing multi-modal data. However, existing FSAR methods often employ simplistic fusion techniques, such as concatenation or comparison, which may not fully leverage the potential of multi-modal information. We aim to explore multi-modal information from different views and subsequently perform multi-view fusion. Based on the textual and visual modality information extracted by the CLIP backbone, we propose an MDMF method that comprehensively utilizes these modalities at two levels. At the first level, we extract visual information from two views: Local Temporal Context and Global Temporal Context. Within each view, we fuse visual features with textual information through concatenation and Cross-Transformer operations. We then employ metric comparison to derive probability distributions for classification under the meta-learning paradigm for each view. At the second level, we fuse the probability distributions from both views to make the final decision. Concurrently, during training, for each query, we calculate the posterior distributions of the textual and visual modalities within each view using text information distance and visual information distance. Based on these distributions, we group query samples with higher view reliability. Subsequently, we enhance the representation of the less reliable view of specific samples through mutual distillation. By delving deep into multi-modal data through a multi-view approach, our few-shot action recognition model demonstrates the potential for achieving higher accuracy and enhanced robustness. Our code is available at the URL: https://github.com/cofly2014/CLIP-MDMF.

Snap&Nav: Smartphone-based Indoor Navigation System For Blind People via Floor Map Analysis and Intersection Detection

We present Snap&Nav, a navigation system for blind people in unfamiliar buildings, without prebuilt digital maps. Instead, the system utilizes the floor map as its primary information source for route guidance. The system requires a sighted assistant to capture an image of the floor map, which is analyzed to create a node map containing intersections, destinations, and current positions on the floor. The system provides turn-by-turn navigation instructions while tracking users' positions on the node map by detecting intersections. Additionally, the system estimates the scale difference of the node map to provide distance information. Our system was validated through two user studies with 20 sighted and 12 blind participants. Results showed that sighted participants processed floor map images without being accustomed to the system, while blind participants navigated with increased confidence and lower cognitive load compared to the condition using only cane, appreciating the system's potential for use in various buildings.

Multi-scale locality preserving projection for partial multi-view incomplete multi-label learning

Amidst advancements in feature extraction techniques, research on multi-view multi-label classifications has attracted widespread interest in recent years. However, real-world scenarios often pose a challenge where the completeness of multiple views and labels cannot be ensured. At present, only a handful of techniques have attempted to address the complex issue of partial multi-view incomplete multi-label classification, and the majority of these approaches overlook the significance of manifold structures between instances. To tackle these challenges, we propose a novel partial multi-view incomplete multi-label learning model, termed MSLPP. Differing from existing studies, MSLPP emphasizes retaining the effective inherent structure of data during the feature extraction process, thereby facilitating a richer semantic information extraction. Specifically, MSLPP captures and integrates four types of information: the distance and similarity information in the original feature space, and the distance and similarity information in the extracted feature space. Further, by adopting the graph embedding technique, it simultaneously preserves the intrinsic structure with multi-scale information through a constraint term. Moreover, taking into account the negative impact of the missing views on the model and the possible impact of missing views on the data inherent structure, we further propose a shielding strategy for missing views, which not only eliminates the negative effects of missing views on the model but also more accurately captures the inherent data structure. The experimental results on five widely recognized datasets indicate that the model performs better than many excellent methods.

Digital 3D Hologram Generation Using Spatial and Elevation Information

The evolution of cartography poses challenges in representing three-dimensional terrain accurately on traditional two-dimensional maps. Providing an accurate 3D view of the area, coupled with essential geographic information, is vital for rapid and accurate decision-making in emergency management and response. Holography offers a promising solution by providing immersive three-dimensional visualizations. The field of hologram mapping, although novel, is still developing. Given its nascent stage, several limitations are evident. This study addresses one such limitation—inaccuracies in distance measurement—by presenting a hologram map that integrates two-dimensional and three-dimensional information. Accurate distance information on maps is critical for operational success. We aimed to improve hologram maps by integrating contour lines. Our approach allows users to measure distances from near-perpendicular angles while viewing 3D features from other perspectives. We review current advancements in hologram mapping, highlight existing limitations, and introduce our innovative solution designed to enhance both accuracy and usability. Our experiment resulted in a hologram map that accurately depicts a 3D environment, integrates contour lines, and allows for distance and slope angle measurements. The hologram map fills the research gap by providing accurate 3D visualization and distance measurement, signifying a major advancement in hologram mapping.

Design of a Near-Field Synthetic Aperture Radar Imaging System Based on Improved RMA

Traditional near-field synthetic aperture radar (SAR) imaging algorithms reveal target features by exploiting signal amplitude and phase information. However, electromagnetic wave propagation is constrained by short distance. Therefore, the spherical wave approximation needs to be considered. In addition, it is also limited by equipment ambient noise, azimuth-distance coupling, wave scattering, and transmission power. Both the amplitude and phase of the signal suffer from the interference of multiple clutter, so they cannot be effectively utilized. To address these issues, this paper introduces a covering penetration detection system based on an improved Range Migration Algorithm (IMRMA) imaging method. Firstly, the proposed method minimizes interferences from the front end of the system using an optimized window to balance denoising and information preservation. Next, interval non-uniform interpolation, instead of Stolt interpolation decoupling, is employed to reduce the computational overhead significantly. To minimize the effects due to wave scattering and propagation loss, distance information is enhanced using amplitude and phase compensation. This reduces scattering effects and enhances image quality. An experimental system is constructed based on a vector network analyzer (VNA) to image the target. The proposed method takes about half the time of traditional RMA. The PSNR in the chunky bowl experiment is higher than 14 dB, which is higher than all the compared methods in the paper. The test results show that the designed system and the reported method can effectively achieve high-resolution images by strengthening the target intensity and suppressing the environmental artifacts.

An extended group decision-making algorithm with intuitionistic fuzzy set information distance measures and their applications

The intuitionistic fuzzy set is a generalization of the fuzzy set that performs noticeably better in expressing and managing uncertainty. The amount that one intuitionistic fuzzy set differs from the others is given by the distance measure. Certain distance measures that have been suggested by the various researchers do not satisfy the axioms of distance measures and also be counter-intuitive circumstances. In this paper we present a novel distance measure for intuitionistic fuzzy sets that is based on the difference between the cross-evaluation factor’s minimum and maximum, the membership degree and non-membership degree, respectively. The proposed measure satisfies all the axiomatic properties and also resolves the counter-intuitive cases. Consequently, this study provides an efficient symmetric distance formula for determining the distance between the information contained by intuitionistic fuzzy sets. By using numerical examples, it is shown that the new measurement is reliable. Also, we provide pattern recognition algorithms and employ them to solve diagnostic-related problems in medicine.

Synthetic oversampling with Mahalanobis distance and local information for highly imbalanced class-overlapped data

Minority oversampling is currently one of the most popular and effective methods for handling imbalanced data. However, oversampling that relies on the observations of the minority class to generate new samples is not applicable in the scenario of imbalanced data with extremely scarce minority samples, because the strongly underrepresented minority class does not contain enough information to support the oversampling process. Since some recent studies have exhibited the effectiveness of using majority information to bootstrap oversampling, the neglect of class overlap in the sampling process would increase the overlapping degree and complicate the decision boundary. To this end, this paper proposes a Mahalanobis distance and Local information based OverSampling (MLOS) for highly imbalanced class-overlapped data. MLOS first employs the majority density to guide the sample synthesis, with Mahalanobis distance to extract the majority probability contour. Then for each minority seed sample, to avoid the generation of overlapping samples, MLOS constrain the synthetic process by finding the auxiliary sample (in its 5 nearest neighbors) with similar probability density value to the seed. Finally, MLOS uses a pair-wise data cleaning process to improve the visibility of the decision boundary according to the probability density of synthetic samples. Comparative experiments conducted on 16 highly imbalanced class-overlapped datasets, using 17 different methods, demonstrates the superiority of our proposed method in terms of three popular evaluation metrics AUC, G-mean and Recall for imbalance classification. The source code of MLOS is available at https://github.com/ytyancp/MLOS.

Planning under uncertainty for safe robot exploration using Gaussian process prediction

The exploration of new environments is a crucial challenge for mobile robots. This task becomes even more complex with the added requirement of ensuring safety. Here, safety refers to the robot staying in regions where the values of certain environmental conditions (such as terrain steepness or radiation levels) are within a predefined threshold. We consider two types of safe exploration problems. First, the robot has a map of its workspace, but the values of the environmental features relevant to safety are unknown beforehand and must be explored. Second, both the map and the environmental features are unknown, and the robot must build a map whilst remaining safe. Our proposed framework uses a Gaussian process to predict the value of the environmental features in unvisited regions. We then build a Markov decision process that integrates the Gaussian process predictions with the transition probabilities of the environmental model. The Markov decision process is then incorporated into an exploration algorithm that decides which new region of the environment to explore based on information value, predicted safety, and distance from the current position of the robot. We empirically evaluate the effectiveness of our framework through simulations and its application on a physical robot in an underground environment.

Bayesian 3D User Localization and Channel Reconstruction with Planar Extremely Large-Scale Antenna Array

An extremely large-scale antenna array (ELAA) can potentially provide significantly increased spatial multiplexing and beamforming gains, as well as enhanced localization capability. While presenting new potential, its near-field propagation and spatial non-stationary properties also impose a great challenge on the receiver design. This paper focuses on the receiver design in an uplink orthogonal frequency-division multiplexing system with a planar ELAA deployed at the base station. To solve the challenging problem of 3D user localization and channel estimation with the planar ELAA, a space-frequency user localization and channel reconstruction (SF-ULCR) receiver is proposed. Under the Bayesian framework, an extended probability model is first established, to capture the channel structural information comprehensively, based on which an iterative receiver consisting of three modules is derived: element-wise line spectrum estimation (ELSE), distance parameter estimation (DPE), and near-field localization (NFL). In particular, the ELSE module handles the line spectrum relationships among multiple subcarriers in the frequency domain, the DPE module extracts and integrates the distance information from the line spectrum parameters, and the NFL module utilizes the messages of distances for user localization based on near-field spatial characteristics. Our numerical results demonstrate that the proposed SF-ULCR algorithm outperforms existing baselines in terms of channel estimation and localization performance, and that it approaches the Cramèr–Rao bound.

Three-dimensional ghost imaging via the Scheimpflug detection.

For previous three-dimensional ghost imaging, the acquisition of absolute distance information is mainly based on the principle of time-of-flight, which usually needs lots of measurements and a large detection/modulation bandwidth product. Here we present a technique called three-dimensional ghost imaging via the Scheimpflug detection (3D-GISD), which exploits the principle of a similar binocular stereoscopic vision for distance information acquisition and can dramatically reduce the measurements required for high-quality 3D image reconstruction. The experimental results demonstrate that high-quality 3D-GISD can be still obtained even if the target exceeds the depth of field of Scheimpflug imaging system and less than 500 measurements are adopted for an image with 128×128 pixels. What's more, the ranging accuracy of 0.2 mm can be achieved by 3D-GISD at about 1.1 m detection distance for a real scenario. Factors influencing the accuracy of distance measurement for 3D-GISD are also discussed.

Depth estimation from monocular endoscopy using simulation and image transfer approach

Obtaining accurate distance or depth information in endoscopy is crucial for the effective utilization of navigation systems. However, due to space constraints, incorporating depth cameras into endoscopic systems is often impractical. Our goal is to estimate depth images directly from endoscopic images using deep learning. This study presents a three-step methodology for training a depth-estimation network model. Initially, simulated endoscopy images and corresponding depth maps are generated using Unity based on a colon surface model obtained from segmented computed tomography colonography data. Subsequently, a cycle generative adversarial network model is employed to enhance the realism of the simulated endoscopy images. Finally, a deep learning model is trained using the synthesized endoscopy images and depth maps to estimate depths accurately. The performance of the proposed approach is evaluated and compared against prior studies utilizing unsupervised training methods. The results demonstrate the superior precision of the proposed technique in estimating depth images within endoscopy. The proposed depth estimation method holds promise for advancing the field by enabling enhanced navigation, improved lesion marking capabilities, and ultimately leading to better clinical outcomes.

Fisher information bounds and applications to SDEs with small noise

In this paper, we first establish general bounds on the Fisher information distance to the class of normal distributions of Malliavin differentiable random variables. We then study the rate of Fisher information convergence in the central limit theorem for the solution of small noise stochastic differential equations and its additive functionals. We also show that the convergence rate is of optimal order.