Search

The Arnold's Cat Map (ACM) is one of the chaotic transformations, which is utilized by numerous scrambling and encryption algorithms in Information Security. Traditionally, the ACM is used in image scrambling whereby repeated application of the ACM matrix, any image can be scrambled. The transformation obtained by the ACM matrix is periodic; therefore, the original image can be reconstructed using the scrambled image whenever the elements of the matrix, hence the key, is known. The transformation matrices in all the chaotic maps employing ACM has limitations on the choice of the free parameters which generally require the area-preserving property of the matrix used in transformation, that is, the determinant of the transformation matrix to be ± 1. This reduces the number of possible set of keys which leads to discovering the ACM matrix in encryption algorithms using the brute-force method. Additionally, the period obtained is small which also causes the faster discovery of the original image by repeated application of the matrix. These two parameters are important in a brute-force attack to find out the original image from a scrambled one. The objective of the present study is to increase the key space of the ACM matrix, hence increase the security of the scrambling process and make a brute-force attack more difficult. It is proved mathematically that area-preserving property of the traditional matrix is not required for the matrix to be used in scrambling process. Removing the restriction enlarges the maximum possible key space and, in many cases, increases the period as well. Additionally, it is supplied experimentally that, in scrambling images, the new ACM matrix is equivalent or better compared to the traditional one with longer periods. Consequently, the encryption techniques with ACM become more robust compared to the traditional ones. The new ACM matrix is compatible with all algorithms that utilized the original matrix. In this novel contribution, we proved that the traditional enforcement of the determinant of the ACM matrix to be one is redundant and can be removed. [ABSTRACT FROM AUTHOR]

Copyright of Geomatics & Information Science of Wuhan University is the property of Geomatics & Information Science of Wuhan University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This study presents a new approach to generate the transformation matrix for Arnold's Cat Map (ACM). Matrices of standard and modified ACM are well known by many users. Since the structure of the possible matrices is known, one can easily select one of them and use it to recover the image with several trials. However, the proposed method generates a larger set of transform matrices. Thus, one will have difficulty in estimating the transform matrix used for scrambling. There is no fixed structure for our matrix as in standard or modified ACM, making it much harder for the transform matrix to be discovered. It is possible to use different type, order and number of operations to generate the transform matrix. The quality of the shuffling process and the strength against brute-force attacks of the proposed method is tested on several benchmark images. [ABSTRACT FROM AUTHOR]

This work considers the path planning problem of personal mobility vehicle (PMV) for indoor navigation using the Artificial Potential Field (APF) method. The APF method sometimes suffers from an infinite loop problem during the planning phase when the goal is blocked by obstacles with certain characteristics. To address the issue, this study deploys the map augmentation method for replanning. When infinite loop situations occur, the map is transformed and the search for drivable path is initiated. The method successfully generates a feasible trajectory when the map is rotated at a certain angle. The scenario of successful planning is shown in the result. [ABSTRACT FROM AUTHOR]

Robot Exploration can be used to autonomously map an area or conduct search missions in remote or hazardous environments. Using multiple robots to perform this task can improve efficiency for time-critical applications. In this work, a distributed method for multi-robot exploration using a Harmonic Map Transformation (HMT) is presented. We employ SLAM to construct a map of the unknown area and utilize map merging to share terrain information amongst robots. Then, a frontier allocation strategy is proposed to increase efficiency. The HMT is used to safely navigate the robots to the frontiers until the exploration task is complete. We validate the efficacy of the proposed strategy via tests in simulated and real-world environments. Our method is compared to other recent schemes for multi-robot exploration and is shown to outperform them in terms of total path distance. [ABSTRACT FROM AUTHOR]

We study the asymmetric brownian energy, a model of heat conduction defined on the one-dimensional finite lattice with open boundaries. The system is shown to be dual to the symmetric inclusion process with absorbing boundaries. The proof relies on a non-local map transformation procedure relating the model to its symmetric version. As an application, we show how the duality relation can be used to analytically compute suitable exponential moments with respect to the stationary measure. [ABSTRACT FROM AUTHOR]

The clarity of algal images is crucial for solving problems such as harmful algal blooms (HABs) classification and red tide warning. However, the original underwater microscopic images of HABs often produce color distortion and blurred details due to the effects of seawater quality, illumination and image extraction techniques. To deal with the above problems, an enhancement computing method based on cyclic color compensation and three-image Multi-scale fusion is proposed in this paper. In this method, first, we propose a novel cyclic color compensation method to correct the color of the microscopic images of HABs. Then, with the input of three images derived from the color compensation versions of the original images of the algae, the correlation weight maps of the three inputs are redefined to facilitate the transfer of texture features and color contrast to the output images. Finally, to avoid image artifacts in the low-frequency components of the output image after the weight map transformation, a novel image Multi-scale fusion strategy is used in this paper. Qualitative and quantitative experimental results show that the underwater microscopic images of HABs enhanced with the method proposed in this paper have the best texture clarity and global contrast, and the method significantly improves the accuracy of transmission map estimation, edge detection and key point matching in processing algal images. [ABSTRACT FROM AUTHOR]

Purpose: Industry 4.0 is characterised by the exponential pace of technological innovations compelling organisations to transform or be displaced. Industry 4.0 transformation of construction enterprises lacks systematic guidance and notable earlier studies have utilised maturity models to map transformation of enterprises. This paper proposes a conceptual maturity model for construction enterprises for business scenarios leading to Industry 4.0. Design/methodology/approach: The requirements for designing maturity models, including comparison with existing models and scientifically documenting the design process, make Systematic Literature Reviews (SLR) appropriate. Two systematic literature reviews (SLRs) are conducted to shortlist a total of 95 papers, which are subjected to subsequent content analysis. Findings: The first SLR identifies the following process categories as critical levers of industry 4.0 maturity; data management, people and culture, leadership and strategy, collaboration and communication, automation, innovation and change management. The second SLR ascertains that the existing maturity models in construction literature do not adequately correspond to Industry 4.0 business scenarios with limited emphasis on data management, automation, change management and innovation. The findings are assimilated to propose a conceptual Smart Modern Construction Enterprise Maturity Model (SMCeMM). Originality/value: The paper systematises the transformation of construction enterprises in Industry 4.0 and leads to state-of-the-art development of Industry 4.0 and maturity model research in construction. The proposed conceptual model addressed both the demands of the construction industry as well as what is required to navigate Industry 4.0 better. [ABSTRACT FROM AUTHOR]

Three-dimensional coordinate transformations are required to harmonise different types of geospatial data accurately. Developing a mathematical model for data fusion relies on ground control points that produce discrepancies between the physical reality and depicted elements. The disparities between the two coordinate systems are known as the grid-to-ground issue that can be minimised by grid-to-grid or map-to-map transformation. This study develops simplified and rapid models for map-matching with global coordinates using regularised regression approaches to improve the accuracy and reliability of geospatial data. The results indicated that the proposed approach provides superior performance and employs any area with high accuracy. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Zhejiang University: Science A is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

When detecting sea-sky line (SSL) in the infrared image, the blurry SSL, conspicuous sea clutter affects the accurate detection of SSL seriously. To solve these problems, we proposed a robust SSL detection algorithm based on the vertical grayscale distribution feature (VGDF). We divided the infrared image into sub-image blocks by sliding window. The sub-image blocks that contain SSL in the central area are labeled as positive samples, and those without any SSL are labeled as negative samples. To improve the separability of the samples, the vertical grayscale distribution feature map (VGDF map) transformation method is proposed to transform the gray sub-image blocks into the feature maps. The VGDF maps are used as the input of the convolutional neural network to train the SSL recognition model. This strategy can improve the separability of SSL image blocks from background image blocks. Then, we use the trained model to obtain the edge candidates and construct the SSL probability feature map. Finally, we detect the SSL by fitting a straight line with the greatest probability on the SSL probability feature map. The proposed algorithm realized 99.4% accuracy rate on the dataset containing 1320 frames of infrared images. The comparison results showed that our algorithm obtained higher detection accuracy than the existing state-of-the-art algorithms. Our algorithm performs well even when the SSL was blurred or there are obvious ship's wave wakes on the sea surface. [ABSTRACT FROM AUTHOR]

The following article discusses current trends in digital methods used in historical cartographic source editing. Maps are often used not only by scholars interested in spatial history, but also by the general public. Due to increasing popularity of 'maps online', it is crucial to discuss, evaluate and classify current solutions and applications used in cartographic source editing. In the article the 'edition' is understood as the 'critical representation of a historical documents', and many types of map editions are considered. The level of map transformation: from raw image, through georeferenced map to spatial database constitutes the main axis of the division between various types of editions. On the very basic level (maps as images: digital libraries and collections), we only have a raster representation of a historic map, often supplemented by bibliographic metadata and the possibility to download it. The second level is geoportals with georeferenced maps, i.e. with spatial adjustment to modern geographic coordinates. The third, most complex level, includes elaboration of the spatial database with most important features (e.g. settlements, roads, land cover, etc.), which serves as the geographic index. The higher the map transformation level, the more useful the edition becomes. Nevertheless, more complex methods in map processing also should include more documentation about this process which is not always present within discussed projects. The main result of the article, apart from classifying existing applications and proposing their general types is to identify functionality and components of the so-called model edition of a historic map. [ABSTRACT FROM AUTHOR]

With the rapid development of robotics and in-depth research of automatic navigation technology, mobile robots have been applied in a variety of fields. Map construction is one of the core research focuses of mobile robot development. In this paper, we propose an autonomous map calibration method using visible light positioning (VLP) landmarks and Simultaneous Localization and Mapping (SLAM). A layout map of the environment to be perceived is calibrated by a robot tracking at least two landmarks mounted in the venue. At the same time, the robot's position on the occupancy grid map generated by SLAM is recorded. The two sequences of positions are synchronized by their time stamps and the occupancy grid map is saved as a sensor map. A map transformation method is then performed to align the orientation of the two maps and to calibrate the scale of the layout map to agree with that of the sensor map. After the calibration, the semantic information on the layout map remains and the accuracy is improved. Experiments are performed in the robot operating system (ROS) to verify the proposed map calibration method. We evaluate the performance on two layout maps: one with high accuracy and the other with rough accuracy of the structures and scale. The results show that the navigation accuracy is improved by 24.6 cm on the high-accuracy map and 22.6 cm on the rough-accuracy map, respectively. [ABSTRACT FROM AUTHOR]

Videos represent the most prevailing form of digital media for communication, information dissemination, and monitoring. However, their widespread use has increased the risks of unauthorised access and manipulation, posing significant challenges. In response, various protection approaches have been developed to secure, authenticate, and ensure the integrity of digital videos. This study provides a comprehensive survey of the challenges associated with maintaining the confidentiality, integrity, and availability of video content, and examining how it can be manipulated. It then investigates current developments in the field of video security by exploring two critical research questions. First, it examine the techniques used by adversaries to compromise video data and evaluate their impact. Understanding these attack methodologies is crucial for developing effective defense mechanisms. Second, it explores the various security approaches that can be employed to protect video data, enhancing its transparency, integrity, and trustworthiness. It compares the effectiveness of these approaches across different use cases, including surveillance, video on demand (VoD), and medical videos related to disease diagnostics. Finally, it identifies potential research opportunities to enhance video data protection in response to the evolving threat landscape. Through this investigation, this study aims to contribute to the ongoing efforts in securing video data, providing insights that are vital for researchers, practitioners, and policymakers dedicated to enhancing the safety and reliability of video content in our digital world. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicle (UAV) image object detection is a challenging task, primarily due to various factors such as multi-scale objects, a high proportion of small objects, significant overlap between objects, poor image quality, and complex and dynamic scenes. To address these challenges, several improvements were made to the YOLOv8 model. Firstly, by pruning the feature mapping layers responsible for detecting large objects in the YOLOv8 model, significant reduction in computational resources was achieved, rendering the model more lightweight. Simultaneously, a detection head fused with self-attention was introduced simultaneously to enhance the detection capability for small objects. Secondly, the introduction of space depth convolution in place of the original convolutional striding and pooling operations facilitates more effective preservation of details in low-resolution images and small objects. Lastly, a multi-level feature fusion module was designed to merge feature maps from different network layers, enhancing the network's representation capability. Results on the Visdrone dataset demonstrate that the proposed model achieved a significant 4.7% improvement in mAP50 compared to YOLOv8, while reducing the parameter count to only 39% of the original model. Moreover, transfer experiments on the TT100k dataset showed a 3.2% increase in mAP50, validating the effectiveness of the improved model for small object detection tasks in UAV images. Our code is made available at . [ABSTRACT FROM AUTHOR]

Potholes and other road surface damages pose significant risks to vehicles and traffic safety. The current methods of in situ visual inspection for potholes or cracks are inefficient, costly, and hazardous. Therefore, there is a pressing need to develop automated systems for assessing road surface conditions, aiming to efficiently and accurately reconstruct, recognize, and locate potholes. In recent years, various methods utilizing (a) computer vision, (b) three-dimensional (3D) point clouds, or (c) smartphone data have been employed to map road surface quality conditions. Machine learning and deep learning techniques have increasingly enhanced the performance of these methods. This review aims to provide a comprehensive overview of cutting-edge computer vision and machine learning algorithms for pothole detection. It covers topics such as sensing systems for acquiring two-dimensional (2D) and 3D road data, classical algorithms based on 2D image processing, segmentation-based algorithms using 3D point cloud modeling, machine learning, deep learning algorithms, and hybrid approaches. The review highlights that hybrid methods combining traditional image processing and advanced machine learning techniques offer the highest accuracy in pothole detection. Machine learning approaches, particularly deep learning, demonstrate superior adaptability and detection rates, while traditional 2D and 3D methods provide valuable baseline techniques. By reviewing and evaluating existing vision-based methods, this paper clarifies the current landscape of pothole detection technologies and identifies opportunities for future research and development. Additionally, insights provided by this review can inform the design and implementation of more robust and effective systems for automated road surface condition assessment, thereby contributing to enhanced roadway safety and infrastructure management. [ABSTRACT FROM AUTHOR]

In this paper, we proposed Mix-VIO, a monocular and binocular visual-inertial odometry, to address the issue where conventional visual front-end tracking often fails under dynamic lighting and image blur conditions. Mix-VIO adopts a hybrid tracking approach, combining traditional handcrafted tracking techniques with Deep Neural Network (DNN)-based feature extraction and matching pipelines. The system employs deep learning methods for rapid feature point detection, while integrating traditional optical flow methods and deep learning-based sparse feature matching methods to enhance front-end tracking performance under rapid camera motion and environmental illumination changes. In the back-end, we utilize sliding window and bundle adjustment (BA) techniques for local map optimization and pose estimation. We conduct extensive experimental validations of the hybrid feature extraction and matching methods, demonstrating the system's capability to maintain optimal tracking results under illumination changes and image blur. [ABSTRACT FROM AUTHOR]

The Arnold Cat Map (ACM) is a popular chaotic map used in image encryption. Chaotic maps are known for their sensitivity to initial conditions and their ability to permute, or rearrange, pixels. However, ACM is periodic, and its period is relatively short. This periodicity decreases the effective key-space and security of a cryptosystem using ACM. Further, ACM is typically only able to be performed on square images. To solve the low periodicity and typical limitation to square images, this paper proposes performing ACM on overlapping square partitions which cover the entirety of an image. The presence of overlap results in a greatly increased image period. The resulting system will be referred to as overlapping ACM or OACM. Several papers have already discussed systems involving overlapping ACM. However, they did not discuss the implementation or periodicity of such a system in detail. This paper does cover the implementation and periodicity analysis of OACM and proposes a simple symmetric encryption system which uses OACM. The proposed encryption system is not as sophisticated or secure as other modern encryption schemes, since it is mainly intended as an initial test of OACM's utility. Histogram and sensitivity analyses did however indicate a level of security against various cryptographic attacks, and OACM performed reasonably in both the permutation and diffusion stages of the cryptosystem. [ABSTRACT FROM AUTHOR]

This paper presents a comparative analysis on random number generation methods based on continuous-time chaos and Poincaré map transformations. Chaotic systems are appealing to use in random number generators (RNGs) as they suggest high throughput data without need for statistical post-processing, and they can be implemented as electrical circuits with relatively less complex hardware. In this study, four commonly used chaos-based random bit generation methods have been analyzed in a comparative manner: (1) regular sampling of chaotic waveform (RSCW), (2) Chaotic sampling of chaotic waveform (CSCW), (3) chaotic sampling of regular waveform (CSRW), and (4) chaos-modulated dual oscillator architecture (CMDOA). A double-scroll attractor system is chosen as the chaotic oscillator and it is numerically simulated in normalized time domain to generate random bit sequences using each of the aforementioned methods. The concepts of autocorrelation and approximate entropy are used to assess the randomness of the generated output bitstreams and to make quantitative comparisons between the bit generation methods. The chaos-based random bit generation methods are compared in terms of three aspects: (1) data throughput capability, (2) robustness against changes in chaos-controlling parameters, (3) robustness against external interference. It is demonstrated that chaotic sampling of chaotic waveform method which is based on Poincaré map transformation provides higher robustness against external interference in comparison with other three methods. On the other hand, chaos-modulated dual oscillator architecture method, which is based on the times related to the Poincaré map, is shown to enable the highest data throughput among the aforementioned chaos-based bit generation methods. Additionally, this paper presents general guidelines which can be applied to the design of random number generators based on continuous-time chaotic oscillators. [ABSTRACT FROM AUTHOR]

In human computer interaction, proxemics describes the ways that people use space to interact with other people or objects. We focus on proxemic maps, which are virtual maps in immersive environments that react to proxemic interaction. Proxemic maps take advantage of new opportunities brought about by immersive visualization, where virtual maps can be freely positioned in virtual or physical space and adapt themselves relative to the spatial position of the viewer. We discuss proxemic interactions that alter the content and type of maps, including changing scale, symbolization, type of visualization and geometry. We propose a novel transformation that changes the geometry of maps based on their proximity to users. Users move the map back and forth and the map transitions between ring, horizontal, vertical and cylindrical geometries. The ring geometry surrounds the user and aligns features on the map with features in the real world. We implemented the map transformation in virtual reality and conducted a user study to evaluate it. The results of the user study indicate that participants preferred the ring and horizontal geometries. The ring geometry is useful because it simplifies connecting virtual features on the map with real features in the landscape, while the horizontal geometry provides an overall view of the landscape. We further found that combination of different geometries helped the study participants to overcome the limitations of each geometry. [ABSTRACT FROM AUTHOR]

A novel model is presented in this study for the situation of wave interference in a non-local stimulated semiconductor medium, including thermal, plasma, and elastic waves. Moisture diffusion in one dimension (1D) has been included in the governing equations by varying the non-local medium's thermal conductivity under the impact of laser pulse according to the non-Gaussian temporal profile. The dimensionless model was characterized by linear transformations. Analytical solutions were obtained by applying Laplace transforms to a description of the governing equations based on the photo-thermoelasticity theory based on moisture diffusivity and variable thermal conductivity. When applying certain of the circumstances (thermal ramp type and non-Gaussian plasma shock), linear solutions are achieved in the time domain using various numerical techniques based on inverse Laplace transforms. Some of the physical variables being studied are visually shown via numerical simulation. The present investigation has produced numerous important concrete instances. Using graphs and theoretical descriptions, several contrasts are drawn, while certain physical factors, such as relaxation durations, changing thermal conductivity, non-local parameters, and reference moisture values, are in play. [ABSTRACT FROM AUTHOR]

The Variable Aquila Optimization Algorithm, designed especially for Multi-Robot Search in uncharted territory, is introduced in this paper. The framework, called Coordinated Multi-robot Exploration Variable Aquila Optimizer (CME-VAO), combines a modified version of the Aquila Optimizer (AO) swarm-based approach with deterministic Coordinated Multi-agent Exploration (CME). With the introduction of stochastic parameters, this integration dynamically adapts the traditional Aquila optimization technique to increase exploration rates. Deterministic CME, which assesses nearby cells surrounding the agents to ascertain cost and utility values, is the first step in the CME-VAO architecture. Exploration efficiency is then further enhanced via Variable Aquila optimization. The effectiveness of the suggested method was confirmed using comprehensive simulations conducted in various environmental settings. Some newer algorithms, like CME-Aquila and CME-Grey Wolf Optimizer (CME-GWO), were used to compare the results. Things like exploration time, failed runs, and area coverage in different conditions were taken into account. We used many simulations with different environmental conditions to find the average coverage percentage and exploration time so that we could compare them statistically with CME-AO and CME-GWO. Results highlight the unique benefit of the suggested algorithm, exhibiting enhanced map exploration with much shorter execution times and few fail runs. All things considered, the CME-VAO architecture significantly improves map navigation in crowded areas in less time during exploration. This puts the suggested methodology in a highly advantageous position for on-board use in dynamic contexts where extended convergence periods or failures of traditional optimization methods are possible. [ABSTRACT FROM AUTHOR]

Counting people in crowd scenarios is extensively conducted in drone inspections, video surveillance, and public safety applications. Today, crowd count algorithms with supervised learning have improved significantly, but with a reliance on a large amount of manual annotation. However, in real world scenarios, different photo angles, exposures, location heights, complex backgrounds, and limited annotation data lead to supervised learning methods not working satisfactorily, plus many of them suffer from overfitting problems. To address the above issues, we focus on training synthetic crowd data and investigate how to transfer information to real-world datasets while reducing the need for manual annotation. CNN-based crowd-counting algorithms usually consist of feature extraction, density estimation, and count regression. To improve the domain adaptation in feature extraction, we propose an adaptive domain-invariant feature extracting module. Meanwhile, after taking inspiration from recent innovative meta-learning, we present a dynamic- β MAML algorithm to generate a density map in unseen novel scenes and render the density estimation model more universal. Finally, we use a counting map refiner to optimize the coarse density map transformation into a fine density map and then regress the crowd number. Extensive experiments show that our proposed domain adaptation- and model-generalization methods can effectively suppress domain gaps and produce elaborate density maps in cross-domain crowd-counting scenarios. We demonstrate that the proposals in our paper outperform current state-of-the-art techniques. [ABSTRACT FROM AUTHOR]

Convolutional neural networks (CNNs) evolved from Fukushima's neocognitron model, which is based on the ideas of Hubel and Wiesel about the early stages of the visual cortex. Unlike other branches of neocognitron-based models, the typical CNN is based on end-to-end supervised learning by backpropagation and removes the focus from built-in invariance mechanisms, using pooling not as a way to tolerate small shifts but as a regularization tool that decreases model complexity. These properties of end-to-end supervision and flexibility of structure allow the typical CNN to become highly tuned to the training data, leading to extremely high accuracies on typical visual pattern recognition data sets. However, in this work, we hypothesize that there is a flip side to this capability, a hidden overfitting. More concretely, a supervised, backpropagation based CNN will outperform a neocognitron/map transformation cascade (MTC) when trained and tested inside the same data set. Yet if we take both models trained and test them on the same task but on another data set (without retraining), the overfitting appears. Other neocognitron descendants like the What-Where model go in a different direction. In these models, learning remains unsupervised, but more structure is added to capture invariance to typical changes. Knowing that, we further hypothesize that if we repeat the same experiments with this model, the lack of supervision may make it worse than the typical CNN inside the same data set, but the added structure will make it generalize even better to another one. To put our hypothesis to the test, we choose the simple task of handwritten digit classification and take two well-known data sets of it: MNIST and ETL-1. To try to make the two data sets as similar as possible, we experiment with several types of preprocessing. However, regardless of the type in question, the results align exactly with expectation. [ABSTRACT FROM AUTHOR]

Forest resources are one of the indispensable resources of the earth, which are the basis for the survival and development of human society. With the swift advancements in computer vision and artificial intelligence technology, the utilization of deep learning for smoke detection has achieved remarkable results. However, the existing deep learning models have poor performance in forest scenes and are difficult to deploy because of numerous parameters. Hence, we introduce an optimized forest fire smoke monitoring system for embedded edge devices based on a lightweight deep learning model. The model makes full use of the multi-scale variable attention mechanism of Transformer architecture to strengthen the ability of image feature extraction. Considering the needs of application scenarios, we propose an improved lightweight network model LCNet for feature extraction, which can reduce the parameters and enhance detecting ability. In order to improve running speed, a simple semi-supervised label knowledge distillation scheme is used to enhance the overall detection capability. Finally, we design and implement a forest fire smoke detection system on an embedded device, including the Jetson NX hardware platform, high-definition camera, and detection software system. The lightweight model is transplanted to the embedded edge device to achieve rapid forest fire smoke detection. Also, an asynchronous processing framework is designed to make the system highly available and robust. The improved model reduces three-fourths of the parameters and increases speed by 3.4 times with similar accuracy to the original model. This demonstrates that our system meets the precision demand and detects smoke in time. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new approach for service robots to perform delivery tasks in indoor environments, including map-building and the automatic renewal of destinations for navigation. The first step involves converting the available floor plan (i.e., CAD drawing) of a new space into a grid map that the robot can navigate. The system then segments the space in the map and generates movable initial nodes through a generalized Voronoi graph (GVG) thinning process. As the second step, we perform room segmentation from the grid map of the indoor environment and classify each space. Next, when the delivery object is recognized while searching the set space using the laser and RGB-D sensor, the system automatically updates itself to a position that makes it easier to grab objects, taking into consideration geometric relationships with surrounding obstacles. Also, the system supports the robot to autonomously explore the space where the user's errand can be performed by hierarchically linking recognized objects and spatial information. Experiments related to map generation, estimating space from the recognized objects, and destination node updates were conducted from CAD drawings of buildings with actual multiple floors and rooms, and the performance of each stage of the process was evaluated. From the quantitative evaluation of each stage, the proposed system confirmed the potential of partial automation in performing location-based robot services. [ABSTRACT FROM AUTHOR]

Deep images can provide rich spatial structure information, which can effectively exclude the interference of illumination and road texture in road scene segmentation and make better use of the prior knowledge of road area. This paper first proposes a new cross‐modal feature maintenance and encouragement network. It includes a quantization statistics module as well as a maintenance and encouragement module for effective fusion between multimodal data. Meanwhile, for the problem that if the road segmentation is performed directly using a segmentation network, there will be a lack of supervised guidance with clear physical meaningful information and poor interpretability of learning features, this paper proposes two road segmentation models based on prior knowledge of deep image: disparity information and surface normal vector information. Then, a two‐branch neural network is used to process the colour image and the processed depth image separately, to achieve the full utilization of the complementary features of the two modalities. The experimental results on the KITTI road dataset and Cityscapes dataset show that the method in this paper has good road segmentation performance and high computational efficiency. [ABSTRACT FROM AUTHOR]

To eliminate blind spots in the field of vision and achieve a safe and collision‐free path, this paper proposes a path planning method based on multivehicle collaborative mapping in the context of vehicle networking. First, a multi vehicle map merging strategy based on the fireworks algorithm is proposed. In this strategy, a dissimilarity objective function based on the concept of grid map similarity is established and an improved fireworks algorithm is used to quickly search for the maximum overlap between local maps, achieving multivehicle collaborative mapping. Second, a real‐time path planning method based on artificial potential field theory is proposed. The information obtained from multivehicle collaborative mapping is first combined with the potential field model to form a multifield coupled road environment model. Then, the obstacle repulsion potential field model is improved to address the issues of traditional artificial potential field methods that target unreachability and poor dynamic response. The feasibility and effectiveness of the collaborative path planning method and single vehicle path planning method are tested through simulation analysis. This paper demonstrates through simulation analysis that the proposed path planning method can effectively achieve beyond line of sight perception and safely and comfortably guide vehicles to complete path planning. [ABSTRACT FROM AUTHOR]

Floods pose a significant threat to road networks globally, disrupting transportation, isolating communities, and causing economic losses. This study proposes a four-stage methodology (avoidance, endurance, recovery, and adaptability) to enhance the resilience of road networks. We combine analysis of constructed assets and asset system performance with multiple disaster scenarios (Reactive Flood Response, Proactive Resilience Planning, and Early Warning Systems). Advanced flood Geospatial-AI models and open data sources pinpoint high-risk zones affecting crucial routes. The study investigates how resilient assets and infrastructure scenarios improve outcomes within Urban Resilience Index (CRI) planning, integrating performance metrics with cost–benefit analysis to identify effective and economically viable solutions. A case study on the Lisbon Road network subjected to flood risk analyzes the effectiveness and efficiency of these scenarios, through loss and gain cost analysis. Scenario 2, Proactive Resilience Planning, demonstrates a 7.6% increase compared to Scenario 1, Reactive Flood Response, and a 3.5% increase compared to Scenario 3, Early Warning Systems Implementation. By considering asset performance, risk optimization, and cost, the study supports resilient infrastructure strategies that minimize economic impacts, while enabling communities to withstand and recover from flood events. Integrating performance and cost–benefit analysis ensures the sustainability and feasibility of risk reduction measures. [ABSTRACT FROM AUTHOR]

This article explores detecting and categorizing network traffic data using machine-learning (ML) methods, specifically focusing on the Domain Name Server (DNS) protocol. DNS has long been susceptible to various security flaws, frequently exploited over time, making DNS abuse a major concern in cybersecurity. Despite advanced attack, tactics employed by attackers to steal data in real-time, ensuring security and privacy for DNS queries and answers remains challenging. The evolving landscape of internet services has allowed attackers to launch cyber-attacks on computer networks. However, implementing Secure Socket Layer (SSL)-encrypted Hyper Text Transfer Protocol (HTTP) transmission, known as HTTPS, has significantly reduced DNS-based assaults. To further enhance security and mitigate threats like man-in-the-middle attacks, the security community has developed the concept of DNS over HTTPS (DoH). DoH aims to combat the eavesdropping and tampering of DNS data during communication. This study employs a ML-based classification approach on a dataset for traffic analysis. The AdaBoost model effectively classified Malicious and Non-DoH traffic, with accuracies of 75% and 73% for DoH traffic. The support vector classification model with a Radial Basis Function (SVC-RBF) achieved a 76% accuracy in classifying between malicious and non-DoH traffic. The quadratic discriminant analysis (QDA) model achieved 99% accuracy in classifying malicious traffic and 98% in classifying non-DoH traffic. [ABSTRACT FROM AUTHOR]

Based on an improved generative adversarial networks algorithm (CGAN), this paper explores a technical way to realize map transformation through autonomous learning and training of remote sensing images. Just skip the trial process vector data update and cumbersome process of mapping the basic map elements can be automatically transform, the image on the main streets and typical rules of construction material, can achieve automatic identification and transformation, greatly shorten the tile map production and update cycle, improve the efficiency of the network map service quality. The results of the test platform have proved that it can be applied to a certain extent and can basically meet the requirements of network map production. [ABSTRACT FROM AUTHOR]

The increasing development of cryptocurrencies has brought cryptojacking as a new security threat in which attackers steal computing resources for cryptomining. The digitization of the supply chain is a potential major target for cryptojacking due to the large number of different infrastructures involved. These different infrastructures provide information sources that can be useful to detect cryptojacking, but with a wide variety of data formats and encodings. This paper describes the semantic data aggregator (SDA), a normalization and aggregation system based on data modelling and low-latency processing of data streams that facilitates the integration of heterogeneous information sources. As a use case, the paper describes a cryptomining detection system (CDS) based on network traffic flows processed by a machine learning engine. The results show how the SDA is leveraged in this use case to obtain aggregated information that improves the performance of the CDS. [ABSTRACT FROM AUTHOR]

This review paper provides a structured analysis of obstacle avoidance and route planning algorithms for unmanned surface vehicles (USVs) spanning both numerical simulations and real-world applications. Our investigation encompasses the development of USV route planning from the year 2000 to date, classifying it into two main categories: global and local route planning. We emphasize the necessity for future research to embrace a dual approach incorporating both simulation-based assessments and real-world field tests to comprehensively evaluate algorithmic performance across diverse scenarios. Such evaluation systems offer valuable insights into the reliability, endurance, and adaptability of these methodologies, ultimately guiding the development of algorithms tailored to specific applications and evolving demands. Furthermore, we identify the challenges to determining optimal collision avoidance methods and recognize the effectiveness of hybrid techniques in various contexts. Remarkably, artificial potential field, reinforcement learning, and fuzzy logic algorithms emerge as standout contenders for real-world applications as consistently evaluated in simulated environments. The innovation of this paper lies in its comprehensive analysis and critical evaluation of USV route planning algorithms validated in real-world scenarios. By examining algorithms across different time periods, the paper provides valuable insights into the evolution, trends, strengths, and weaknesses of USV route planning technologies. Readers will benefit from a deep understanding of the advancements made in USV route planning. This analysis serves as a road map for researchers and practitioners by furnishing insights to advance USV route planning and collision avoidance techniques. [ABSTRACT FROM AUTHOR]

Introduction: Despite the many attempts made by researchers to diagnose jaundice non-invasively using machine learning techniques, the low amount of data used to build their models remains the key factor limiting the performance of their models. Objective: To build a system to diagnose neonatal jaundice non-invasively based on machine learning algorithms created based on a dataset comprising 767 infant images using a computer device and a USB webcam. Methods: The first stage of the proposed system was to evaluate the performance of four machine learning algorithms, namely support vector machine (SVM), k nearest neighbor (k-NN), random forest (RF), and extreme gradient boost (XGBoost), based on a dataset of 767 infant images. The algorithm with the best performance was chosen as the classifying algorithm in the developed application. The second stage included designing an application that enables the user to perform jaundice detection for a patient under test with the minimum effort required by capturing the patient's image using a USB webcam. Results: The obtained results of the first stage of the machine learning algorithms evaluation process indicated that XGBoost outperformed the rest of the algorithms by obtaining an accuracy of 99.63%. The second-best algorithm was the RF algorithm, which had an accuracy of 98.99%. Following RF, with a slight difference, was the k-NN algorithm. It achieved an accuracy of 98.25%. SVM scored the lowest performance among the above three algorithms, with an accuracy of 96.22%. Based on these obtained results, the XGBoost algorithm was chosen to be the classifier of the proposed system. In the second stage, the jaundice application was designed based on the model created by the XGBoost algorithm. This application ensured it was user friendly with as fast a processing time as possible. Conclusion: Early detection of neonatal jaundice is crucial due to the severity of its complications. A non-invasive system using a USB webcam and an XGBoost machine learning technique was proposed. The XGBoost algorithm achieved 99.63% accuracy and successfully diagnosed 10 out of 10 NICU infants with very little processing time. This denotes the efficiency of machine learning algorithms in healthcare in general and in monitoring systems specifically. [ABSTRACT FROM AUTHOR]

The planning of interventions aimed at preserving the built heritage of inner areas is a complex process due to the fragility of these contexts. It should stem from adequate knowledge to support the recognition of qualities, resources, and potentials, and the reinterpretation of residual values. From the perspective of an axiological approach to the built heritage, it is possible to foster the resemantization of such values based on a rigorous cognitive model. This research proposed a cognitive model of the built heritage of the historic neighbourhood of Granfonte in Leonforte (Enna). The knowledge model, developed in Excel, has a hierarchical type of structure characterized by domain, classes, properties, and the attribution of values to properties. The approach makes it possible to execute queries that arise from specific relationships between classes. In this study, we developed both simple queries to measure the percentages of certain characteristics of the building units and complex queries for the estimation of aggregate indices to define the degree of transformation and loss of integrity I T I and degradation I D of the building units or to identify those most exposed to the risk of ruination and contagion. The proposed model can be framed within the framework of ontologies supporting structured heritage knowledge. [ABSTRACT FROM AUTHOR]

Digital transformation has led to a multitude of new technical solutions. This includes Extended Reality technologies: Hardware and software systems that enrich the real environment with information. The area of application extends to the entire value chain and thus offers great potential for companies. A systematic and holistic implementation strategy is necessary to exploit this optimally. XR solutions often remain prototypes mainly because of lack of knowledge management and missing understanding about the technology. In this respect, this article presents a tool for the systematic, holistic strategic consideration of XR from the vision to the planning of measures. The tool presented combines common methods such as the Strategy Map, the Transformation Map or the Balanced Scorecard. Thus, it does not only offer companies a strategic framework, but also includes concrete measures for a sustainable, successful implementation. [ABSTRACT FROM AUTHOR]

Machine vision, an interdisciplinary field that aims to replicate human visual perception in computers, has experienced rapid progress and significant contributions. This paper traces the origins of machine vision, from early image processing algorithms to its convergence with computer science, mathematics, and robotics, resulting in a distinct branch of artificial intelligence. The integration of machine learning techniques, particularly deep learning, has driven its growth and adoption in everyday devices. This study focuses on the objectives of computer vision systems: replicating human visual capabilities including recognition, comprehension, and interpretation. Notably, image classification, object detection, and image segmentation are crucial tasks requiring robust mathematical foundations. Despite the advancements, challenges persist, such as clarifying terminology related to artificial intelligence, machine learning, and deep learning. Precise definitions and interpretations are vital for establishing a solid research foundation. The evolution of machine vision reflects an ambitious journey to emulate human visual perception. Interdisciplinary collaboration and the integration of deep learning techniques have propelled remarkable advancements in emulating human behavior and perception. Through this research, the field of machine vision continues to shape the future of computer systems and artificial intelligence applications. [ABSTRACT FROM AUTHOR]

In this work, the effect of hydrostatic initial stress is studied with the exited semiconductor material. Electrons are exited at the free surface of the elastic semiconductor medium using the photo-thermal-elastic theory during the transport (diffusion) process. The thermal memories are due to the dual-phase-lag (DPL) relaxation times of the heat conduction equation with a novel model. In this model, the thermal conductivity is chosen as a linear dependent function of the thermal temperature impact which is variable. The effect of gravitational field is taken into account during the two-dimensional (2D) elastic deformation under the impact of an internal heat source. The Kirchhoff transformation mapping is used. The harmonic wave (normal mode) method is applied for the 2D governing equations to obtain the main physical fields analytically. Some thermal and mechanical forces are applied with other elastic-plasma conditions at the free surface of the semiconductor material to get the complete solutions. Some comparisons used a novel parameters which depend on the DPL model and the differences in the thermal conductivity parameters and they are illustrated graphically and discussed. [ABSTRACT FROM AUTHOR]

Potholes are one of the most common forms of road damage, which can severely affect driving comfort, road safety, and vehicle condition. Pothole detection is typically performed by either structural engineers or certified inspectors. However, this task is not only hazardous for the personnel but also extremely time consuming. This article presents an efficient pothole detection algorithm based on road disparity map estimation and segmentation. We first incorporate the stereo rig roll angle into shifting distance calculation to generalize perspective transformation. The road disparities are then efficiently estimated using semiglobal matching. A disparity map transformation algorithm is then performed to better distinguish the damaged road areas. Subsequently, we utilize simple linear iterative clustering to group the transformed disparities into a collection of superpixels. The potholes are finally detected by finding the superpixels, whose intensities are lower than an adaptively determined threshold. The proposed algorithm is implemented on an NVIDIA RTX 2080 Ti GPU in CUDA. The experimental results demonstrate that our proposed road pothole detection algorithm achieves state-of-the-art accuracy and efficiency. [ABSTRACT FROM AUTHOR]