Visibility Problems Research Articles

Complete Visibility Algorithms of Luminous Robots With Two‐Color Lights on Grid

ABSTRACTAn autonomous mobile robot system is a distributed system consisting of multiple mobile computational entities, called robots, which autonomously and repeatedly perform three fundamental operations: look, compute, and move. Various challenges in such systems, including gathering, pattern formation, and flocking, have been extensively studied to explore the relationship between the robots' capabilities and the feasibility of solving these problems (i.e., solvability). In this study, we focus on the complete visibility problem, which aims to relocate all robots on an infinite grid plane so that every robot is visible to every other robot (i.e., complete visibility). We assume that each robot is a luminous robot (i.e., has a light with a constant number of colors) and opaque (non‐transparent). This paper primarily examines the number of light colors required for each robot to solve the complete visibility problem. Specifically, we investigate the question: “how many colors can we reduce while still achieving complete visibility?” (even under certain stronger assumptions). As an answer to the above question, we show the existence of a deterministic algorithm to achieve complete visibility (i.e., every robot can observe all the other robots) using only two colors of light, if the robots agree on the directions and orientations of both axes. The proposed algorithm correctly works even if robots operate asynchronously and have no knowledge of the total number of robots. Moreover, its spatial complexity (i.e., the area of the smallest enclosed rectangle that includes all robots in the final configuration) ensures the optimal one, , where is the number of robots.

UICE-MIRNet guided image enhancement for underwater object detection

Underwater object detection is a crucial aspect of monitoring the aquaculture resources to preserve the marine ecosystem. In most cases, Low-light and scattered lighting conditions create challenges for computer vision-based underwater object detection. To address these issues, low-colorfulness and low-light image enhancement techniques are explored. This work proposes an underwater image enhancement technique called Underwater Image Colorfulness Enhancement MIRNet (UICE-MIRNet) to increase the visibility of small, multiple, dense objects followed by underwater object detection using YOLOv4. UICE-MIRNet is a specialized version of classical MIRNet, which handles random increments of brightness features to address the visibility problem. The proposed UICE-MIRNET restrict brightness and also works on the improvement of the colourfulness of underwater images. UICE-MIRNet consists of an Underwater Image-Colorfulness Enhancement Block (UI-CEB). This block enables the extraction of low-colourful areas from underwater images and performs colour correction without affecting contextual information. The primary characteristics of UICE-MIRNet are the extraction of multiple features using a convolutional stream, feature fusion to facilitate the flow of information, preservation of contextual information by discarding irrelevant features and increasing colourfulness through proper feature selection. Enhanced images are then trained using the YOLOv4 object detection model. The performance of the proposed UICE-MIRNet method is quantitatively evaluated using standard metrics such as UIQM, UCIQE, entropy, and PSNR. The proposed work is compared with many existing image enhancement and restoration techniques. Also, the performance of object detection is assessed using precision, recall, and mAP. Extensive experiments are conducted on two standard datasets, Brackish and Trash-ICRA19, to demonstrate the performance of the proposed work compared to existing methods. The results show that the proposed model outperforms many state-of-the-art techniques.

An alternative approach for increasing the visibility of roads

Visibility problems occur on highways that are not sufficiently illuminated at night, endangering traffic safety. Phosphor material, which has a natural glow feature under ultraviolet (UV) light, is planned to increase road visibility in areas with inadequate lighting. Phosphor powder (PP) was used in four different percentages (25%, 50%, 75%, and 100%) as fillers in hot mix asphalt (HMA), reduced to filler size. Asphalt specimens were prepared using a super pave gyratory compactor and super pave volumetric mixture design. Compacted specimens were exposed to artificial 12V UV light for 10-minute intervals in a dark room, and UV light absorption was observed. Visibility analyses were performed on the specimens by taking high-resolution photos with long exposure from a distance of approximately 30 cm from the asphalt specimen using a professional camera. According to the analysis results, the visibility values increased by 200.4%, 378.5%, 538.1%, and 728.5% compared to the reference specimen for substitution rates of 25%, 50%, 75%, and 100%, respectively. Experiments were conducted to determine the behavior of the specimens prepared as phosphorus substitutes in the mixture. After selecting the optimum binder contents, the modified Lottman test procedure was applied to measure the specimens' strength values and moisture sensitivity prepared at optimum ratios. The indirect tensile test results show that the 25% PP-substituted specimen had a better strength value. The tensile strength ratio (TSR) value, the ratio of dry and wet tensile stresses, was determined to have minor moisture sensitivity in the 50% PP-substituted specimen. HWTT was applied to the specimen containing 50% PP content, which exhibited the best TSR ratio, resulting in improved rutting performance compared to the reference specimen.

Analysis of spatial variability of smog episodes over National Capital Delhi during (2013–2017)

Air pollution is a pressing issue in Delhi, with smog occurrences causing reduced visibility and various respiratory problems. A series of severe SMOG (smoke + fog) episodes between 2013 and 2017 with reduced visibility and exceptionally high PM2.5 concentrations have been reported in Delhi especially around Diwali festival (October–November). The Smog of 2016 is referred as Great Smog of Delhi. This study examined remote sensing data from 2013 to 2017 to investigate smog episodes in Delhi during pre-Diwali, post-Diwali, and Diwali. Satellite-derived parameters viz absorbing aerosol index (AAI), aerosol optical depth (AOD), and ozone monitoring instrument (OMI) along with air pollution data and climatic parameters were used to analyze smog episodes. The results showed that during smog episodes, AOD, AAI and PM2.5 concentrations exceeded permissible limits significantly at all stations across Delhi during the Diwali festival. The ground-based observations at different locations across Delhi and satellite data-derived datasets confirmed the severity of smog episodes. The findings indicate that burning of fire crackers coupled with agriculture stubble burning and subsequent transport of the smoke from North Western states through the Capital had a greater impact on deteriorating air quality in Delhi than local pollution, especially during unfavorable weather conditions associated with high humidity and weaker winds. The outcomes highlight the significance of remotely sensed information in identifying smog episodes and their severity in Delhi. It also underlines the necessity for efficient interventions to control air pollution, particularly amid festivals like Diwali.

Mutual visibility of luminous robots despite angular inaccuracy

We initiate the study of the Mutual Visibility problem using N opaque luminous point robots that have inaccurate movements. Each robot operates in Look-Compute-Move cycles and has a persistent light attached to it to have a weak form of communication between robots using a constant number of colors. The inaccuracy for a robot r is an angular deviation from its target point T to a point T′ such that the angle ∠TrT′<90∘. The problem becomes unsolvable if this angle is ≥90∘. From any initial configuration of the robots on the Euclidean plane, the problem aims to arrange the robots in a configuration such that any two robots are visible to each other. We assume that the robots agree on one coordinate axis. We present two collision-free algorithms, a 2 color algorithm (which is optimal in the number of colors used) for semi-synchronous setting and a 3 color algorithm for asynchronous setting, both of which run in O(N) epochs. We also study the problem in the presence of mobile faulty robots. A robot can exhibit both mobility failure and angular inaccuracies in its movement. We present a fault-tolerant algorithm that aims to bring the robots in a configuration where no three non-faulty robots are collinear, and no faulty robot lies between two non-faulty robots. This algorithm uses 10 colors and takes O(N) epochs under asynchronous settings.

TRAFFIC SIGN RECOGNITION IN CHALLENGING WEATHER CONDITIONS USING CONVOLUTIONAL NEURAL NETWORKS

The ability of autonomous driving systems to recognize traffic signs in a variety of environmental conditions is crucial to their reliability. This study uses convolutional neural networks (CNNs) to provide a novel method for improving the accuracy of traffic sign recognition systems under challenging weather situations. The research focuses on developing a CNN-based model that is trained using the augmented German Traffic Sign Recognition Benchmark (GTSRB) dataset, which contains over 50,000 labeled images of road signs across 43 categories. To attempt to overcome the limitations of traditional CNN models under adverse environmental conditions, this work presents adaptive feature extraction layers that are especially made to reduce visibility problems brought on by rain, fog, and snow. By taking a comprehensive approach, the model uses advanced data augmentation methods to simulate different weather scenarios, greatly increasing the diversity of the training dataset. Through an analysis of theoretical and practical aspects, the study demonstrates how CNNs enhance the accuracy and efficiency of road sign detection systems in a different weather condition. Research evaluates the model's effectiveness using metrics such as precision, recall, and F1-score, proving its capability to reduce false positives and accurately detect relevant road sign instances. Additionally, the paper highlights the importance of careful dataset preparation and augmentation, model optimization, and training enhancements to improve the performance of road sign detection systems. The results of this research offer benefits for intelligent transport systems, autonomous driving, and road safety, pointing towards further advancements in precise and dependable road sign recognition technology.

Pixel-wise low-light image enhancement based on metropolis theorem

Images taken in low-light conditions frequently encounter visibility problems, such as severe noise, reduced brightness, and low contrast. This paper introduces an approach to enhance low-light images using the Metropolis Theorem (MT). The method begins by applying a global gamma correction to the input image, followed by transforming the globally corrected image into the HSV (Hue, Saturation, Value - V) domain. To achieve multi-scale decomposition, an application of the MT is proposed, resulting in approximation and detail sub-images of the V component. Subsequently, local gamma correction is employed on both the final approximation and detail images to enhance local contrast. The refined approximation and detail images are then combined to reconstruct the refined V component. The reconstructed image is obtained by weighting each band of the image with the refined V component. Experimental results demonstrate that the proposed method outperforms state-of-the-art methods, providing improved visual quality and more natural colors.

Crescent Moon Visibility: A New Criterion using Deep learned Artificial Neural-Network

Many authors investigated the problem of the early visibility of the new crescent moon after the conjunction and proposed many criteria addressing this issue in the literature. This article presented a proposed criterion for early crescent moon sighting based on a deep-learned pattern recognizer artificial neural network (ANN) performance. Moon sight datasets were collected from various sources and used to learn the ANN. The new criterion relied on the crescent width and the arc of vision from the edge of the crescent bright limb. The result of that criterion was a control value indicating the moon's visibility condition, which separated the datasets into four regions: invisible, telescope only, probably visible, and certainly visible. This criterion was used on the dataset for ANN learning to compare its efficiency with the actual moon visibility events.

JEN-Planet: an Automatically Updating Serious Game Catalogue Designed with and for Teachers

Serious Games (SGs) intended for educational purposes can improve the quality of learning in many contexts and at all levels. However, their use in schools remains minimal despite their many educational and pedagogical benefits. This lack of interest is partly due to educational SGs’ visibility problem. Indeed, in practice, they are mainly indexed in catalogues containing all types of SGs (not necessarily educational). Also, the existing catalogues, often designed by video game editors, do not offer relevant and practical filters to help teachers find SGs adapted to their specific needs. This article proposes JEN-Planet, a catalogue designed from several research contributions to help teachers find existing educational SGs. A comparative study is therefore carried out on its usability, relevance, and utility against those of the two other major catalogues. 50 teachers explored the different catalogues and evaluated them by completing questionnaires. The results show that the JEN-Planet catalogue better meets teachers' needs. This catalogue could therefore improve the visibility of educational SGs for teachers, who will be able to find them more easily.

CONVOLUTIONAL NEURAL NETWORKS: AN APPROACH FOR VISUAL OBSTRUCTION DETECTION IN AUTOMOTIVE REVERSING CAMERAS

In recent years, the study of Artificial Intelligence in the automotive industry has led to the design of intelligent systems applied to road safety, highlighting the importance of improving road safety worldwide, and thus reducing the number of accidents annually. One of the main functions of these systems is, for example, pedestrian detection, which is performed by cameras and radar-type sensors, among others. However, environmental factors cause visibility problems and obstructions that make pedestrian detection difficult and lead to collisions. With the purpose of contributing to the solution of the exposed problem, two case studies using Convolutional Neural Networks are applied in this research. The first using a pre-trained model (Inception V3) and the second, a proposed model (RvlsNet) to detect dirt on the lens of a vehicle's reverse camera. These types of factors directly affect visibility, which leads to an increased risk of collision when reversing the vehicle. Applying a general data mining methodology, we obtained a result of 0.9549 and 0.9416 accuracy, respectively, for the models used. Keywords: Convolutional Neural Networks, Classification, Obstruction, Detection, Reversing camera, Inception V3

Learning to Remove Bad Weather: Towards Robust Visual Perception for Self-Driving

Visual perception plays a vital role in generating the intelligent actions of autonomous vehicles. However, various bad weather degradations can create visibility problems that impair the performance of high-level tasks. Though image enhancement techniques have been extensively studied for safe self-driving in bad weather, few studies have dealt with both enhancement and perception simultaneously. To cope with bad weather conditions for high-level perception, we propose an end-to-end deep learning-based framework, which connects the enhancement network with the perception network. To this end, we design a universal enhancement network that can address multiple bad weather conditions and assist in producing promising perception results. To this end, we design a universal enhancement network that can address multiple bad weather conditions and assist in producing promising perception results. To this end, we design a universal enhancement network that can address multiple bad weather conditions and assist in producing promising perception results.

Current paleoanthropology and paleoarchaeology in the museums of Poland in the tourism context. Invisible heritage?

The heritage of prehistory, in the fields of both palaeoanthropology and palaeoarchaeology, constitutes a huge physical and interpretative resource, even though the majority of artefacts have never left museum storage rooms. The current significant development in research into human fossils does lead to considerations about the current ways of exhibiting museum collections regarding this kind of heritage. In Poland, artefacts of prehistory, including human fossils, are distributed between different kinds of museums ‒ historical, archaeological, natural history, and geological ones, as well as museums belonging to universities and scientific institutes. None of them builds their brand based on palaeoanthropological artefacts. Moreover, since the excavations have stopped, the sites of discoveries of that kind remain illegible to the general public due to the lack of on-site markers and appropriate educational tourist facilities. All these facts together underline the problem of limited visibility of the recent discoveries and palaeoanthropological and palaeoarchaeological heritage in the Polish museum and tourist market.

Coastal archaeological site visibility problems and underwater prospects in the Northern Lake Superior Basin

AbstractThe early to middle Holocene was marked by considerable variability in lake levels in the Lake Superior Basin due to a combination of meltwater runoff, isostatic adjustment, shifting glacial margins, and climate change. These processes likely had a large impact on the preservation and visibility of coastal archaeological sites dating from the Early Period (Paleoindian) to the Middle Period (Shield Archaic). Of particular interest is the brief interval after 9300 cal. B.P. when ancestral Lake Superior dropped to its lowest level (Houghton) and human populations may have made incursions deeper into the basin. Elsewhere in the Upper Great Lakes, this period is associated with offshore archaeological sites submerged by rising water levels later in the Holocene. New geological data from the Thunder Bay, ON, region yield exceptional insight into the paleohydrology of the Houghton phase and, hence, the underwater archaeological prospects of this low water phase in the northern Lake Superior Basin. These data indicate that the lake reached its lowest level by at least ~9100 cal. B.P. but was highly unstable, at least initially, due to a combination of climate and meltwater runoff. Early underwater sites may be confined to two short, hydrologically closed, lowstands between ~9100 and 8700 cal. B.P. and would have been impacted by at least one lake transgression. Such sites, however, may still hold better potential for organic preservation and the visibility of large cultural features compared to their terrestrial counterparts. Coastal sites occupied when the lake was hydrologically closed may be especially well‐preserved due to rapid inundation before the gradual, and generally erosional, Nipissing transgression occurred.

The geodesic mutual visibility problem: Oblivious robots on grids and trees

The Mutual Visibility is a well-known problem in the context of mobile robots. For a set of n robots disposed in the Euclidean plane, it asks for moving the robots without collisions so as to achieve a placement ensuring that no three robots are collinear. For robots moving on graphs, we consider the Geodesic Mutual Visibility (GMV) problem. Robots move along the edges of the graph, without collisions, so as to occupy some vertices that guarantee they become pairwise geodesic mutually visible. This means that there is a shortest path (i.e., a “geodesic”) between each pair of robots along which no other robots reside. We study this problem in the context of trees and (finite or infinite) square grids, for robots operating under the standard Look–Compute–Move model. In both scenarios, we provide resolution algorithms along with formal correctness proofs, highlighting the most relevant peculiarities arising within the different contexts, while optimizing the time complexity.

Assessment and Gifted Discourse in Swedish Early Years Education Steering Documents: The Problem of (In)Visibility

This study explores how assessment is presented in Swedish early years’ steering documents and considers risks for young gifted students in relation to assessment (or lack thereof). Document analysis was undertaken on, firstly, Swedish curriculum documents for the preschool and for the compulsory school, and secondly, mapping materials used in the preschool class with six-year-old children. Results show that assessment is not a term used in Swedish early years curricula. Instead, preschool teachers are asked to evaluate their own practice; preschool class teachers are asked to engage with mapping and only to consider working toward later assessment goals in year 3 of school. A plethora of alternative assessment terms are used in the curriculum without definition. Giftedness is also invisible in the curriculum. However, the mapping materials used with six-year-old students in the subject areas of mathematics and Swedish do encourage teachers to consider children who achieve mastery early. Further, these materials provide supportive questions and activities for teachers to use in exploring further. The specific examples of assessment discourses and the need to consider gifted children are combined in this article to highlight aspects of teacher work that are important for the educational rights of an often-forgotten group of learners.

Exploring the role of ICT in pharmaceutical supply chain practices and operational performance in Ethiopia: a structural equation modeling approach

BackgroundA well-coordinated supply chain ensures the sustainable availability of life-saving medicines that improve public health outcomes. Information Communication Technology (ICT) is one of the strategies for optimizing supply chain coordination. However, there is a paucity of data on how it affects supply chain practice and performance at the Ethiopian Pharmaceutical Supply Agency (EPSA).ObjectiveThis study aimed to explore the relationships between information and communication technology, supply chain practice, and pharmaceutical supply chain operational performance using a structural equation modeling approach.MethodsWe conducted an analytical cross-sectional study between April and June 2021. Three hundred twenty EPSA employees participated in the survey. We used a pretested, self-administered five-point Likert scale questionnaire to collect the intended data. A structural equation modeling confirmed the relationship between the constructs (information communication technology, supply chain practices, and performance). Thus, the measurement models were first validated using exploratory and confirmatory factor analysis in SPSS/AMOS software. A p-value of less than 5% indicated statistical significance.ResultsOf the 320 questionnaires distributed, 300 participants (202 males and 98 females) duly responded. In this survey, supply chain practices (mainly customer relationship management and information sharing) and ICT had significant positive direct effects on operational performance with standardized regression weights (β) of 0.65 (p < .001) and 0.29 (p < .001), respectively. On the other hand, 73% of the variations in operational performance were explained by ICT and supply chain practices, wherein ICT played moderate mediation effects between supply chain practice and performance (VAF = 0.24, p < .001). Despite the significant positive influence of ICT, the agency still faced data visibility problems with customers and other supply chain partners.ConclusionThe findings revealed that supply chain practices and ICT implementation impacted the agency’s supply chain performance positively and significantly. The ICT implementation practice in the agency posited a significant positive partial mediating role between supply chain practice and operational performance. Thus, if the agency focuses on the automation and integration of customer relationship management and the practice of information exchange, the essential supply chain practices, it can further improve operational performance.

Understanding the problem of digital medication inventory visibility in health systems.

This project describes and quantifies the perceived degree of digital visibility to medication inventory throughout 6 large health systems. In this project, 6 large health systems evaluated their physical medication inventory for digital visibility, or the degree to which physical medication inventory information is viewable in electronic systems, during a 2-year period (2019-2020). Inventory reports included medication items with either a National Drug Code (NDC) or a unique institutional identifier. Physical inventory reports contained the medication item name and a corresponding NDC or identifier, the quantity on hand, and the physical locations and the storage environments of the inventory items at the time of the audit. Investigators independently reviewed physical inventory reports and categorized medication line items by degree of digital visibility: (1) no digital visibility, (2) partial digital visibility without accurate quantities, (3) partial digital visibility with accurate quantities, or (4) full digital visibility. Data were anonymized, aggregated, and analyzed to characterize the degree of digital visibility across the health systems and to identify locations and storage environments where the greatest improvement is needed. Overall, less than 1% of medication inventory was judged to have full digital visibility. The majority of the evaluated inventory items were categorized as having partial digital visibility, with or without accurate quantities. Analysis by both units of inventory and inventory valuation indicated that only 30% to 35% of inventory had full digital visibility or partial digital visibility with accurate quantities. Most of the medication inventory within 6 large academic centers is either not digitally visible or partially digitally visible but without accurate quantities. Full digital visibility of inventory is rare. Better digital visibility can minimize disruption from recalls and decrease waste. Technology vendors and health systems must collaborate to develop improved automation and systems to make medications on hand more digitally visible.

Spectroscopic searches for evolutionary orbital period changes in WR + OB binaries: the case of CQ Cep and CX Cep

ABSTRACT We present the results of spectroscopic observations of two eclipsing WR + OB-type systems – CQ Cep and CX Cep – performed in 2020–2023 with a low-resolution slit spectrograph TDS (λλ = 3660–7410 Å, R = 1300–2500) on 2.5-m telescope of the SAI MSU Caucasian Mountain Observatory. For CQ Cep, the radial velocity curves of a WN6 star are constructed, the problem of visibility of spectroscopic traces of an OB star is discussed, and the components’ mass ratio q ∼ 0.6 is estimated. For CX Cep, the radial velocity curves are constructed for both the WN5 and O5 components enabling their masses and circular orbit elements to be refined. The comparison of the radial velocity curves of these systems obtained in different epochs allowed us to derive the orbital period change rate $\dot{P}$ by the spectroscopic method, which is found to be in good agreement with estimates obtained by comparing the moments of primary eclipse minima: $\dot{P} = -0.0151\pm 0.0013$ s yr−1 for CQ Cep and $\dot{P} = 0.054\pm 0.009$ s yr−1 for CX Cep. The prospects of applicability of the spectroscopic dynamical method for studying the orbital evolution of Galactic WR + OB binaries and related objects are considered. We also discuss the effect of finite sizes of stars with stellar wind mass-loss in close binary systems on their orbital evolution.

Mutual visibility by fat robots with slim omnidirectional camera

In the existing literature of the Mutual Visibility problem for autonomous robot swarms, the adopted visibility models have some idealistic assumptions that are not consistent with practical sensing device implementations. This paper investigates the problem in the more realistic visibility model called opaque fat robots with slim omnidirectional camera. The robots are modeled as unit disks, each having an omnidirectional camera represented as a disk of smaller size. The region obstructed by a single robot in this model is a truncated infinite cone. This makes the visibility model significantly challenging compared to the previous ones. We assume that the robots have compasses that allow agreement in the direction and orientation of both axes of their local coordinate systems. The robots are equipped with visible lights which serve as a medium of communication and also as a form of memory. We present a distributed algorithm for the Mutual Visibility problem which is provably correct in the semi-synchronous setting. Our algorithm also provides a solution for Leader Election which we use as a subroutine in our main algorithm. Although Leader Election is trivial with two axis agreement in the full visibility model, it is challenging in our case and is of independent interest.

HydraMarker: Efficient, Flexible, and Multifold Marker Field Generation.

An n-order marker field is a special binary matrix whose n×n subregions are all distinct from each other in four orientations. It is commonly used to guide the composing process of position-sensing markers, which can be detected and identified in a camera image with very limited scope or severe visibility problems. Despite the advantages, position-sensing markers are rare and overlooked because generating marker fields is difficult. In this article, we broaden the definition of marker field, making it more powerful and flexible. Then, we propose bWFC (binary wave function collapse) and its high-speed version, fast-bWFC, to solve the generation problem. The methods are packaged into an open-sourced toolkit named HydraMarker, with which, users not only can generate marker fields on laptops within a short period of time, but also can highly customize them: preset values; fields and subregions in any shape; multifold local uniqueness. Comparative results indicate that the proposed method has superior efficiency, quality, and capability. It makes marker field generation accessible to common marker designers, opening up more possibilities for fiducial markers.