Mapping Algorithm Research Articles

Security authentication scheme based on chebyshev chaotic mapping for library network

With the rapid advancement of information technology, the security of digital libraries, as critical platforms for modern knowledge dissemination, has become increasingly significant. User identity authentication in digital libraries is pivotal to ensuring system security. Traditional authentication methods often have security vulnerabilities and cannot effectively prevent unauthorized access. Therefore, this paper proposes a security authentication scheme based on chebyshev chaotic mapping for library. It addresses security issues in identity authentication in digital libraries. The proposed scheme integrates smart card, password and biometric factor, leveraging the chebyshev chaotic mapping algorithm to achieve stringent user authentication and generate secure session keys, thereby effectively countering security threats. Additionally, the scheme employs physical unclonable function (PUF) to enhance the protection of stored confidential data. The security of this scheme is rigorously analyzed using the Scyther. Security analysis and performance experiments indicate that the scheme not only ensures security but also optimizes authentication efficiency.

High-precision docking of wheelchair/beds through LIDAR and visual information.

To address the low docking accuracy of existing robotic wheelchair/beds, this study proposes an automatic docking framework integrating light detection and ranging (LIDAR), visual positioning, and laser ranging. First, a mobile chassis was designed for an intelligent wheelchair/bed with independent four-wheel steering. In the remote guidance phase, the simultaneous localization and mapping (SLAM) algorithm was employed to construct an environment map, achieving remote guidance and obstacle avoidance through the integration of LIDAR, inertial measurement unit (IMU), and an improved A* algorithm. In the mid-range pose determination and positioning phase, the IMU module and vision system on the wheelchair/bed collected coordinate and path information marked by quick response (QR) code labels to adjust the relative pose between the wheelchair/bed and bed frame. Finally, in the short-range precise docking phase, laser triangulation ranging was utilized to achieve precise automatic docking between the wheelchair/bed and the bed frame. The results of multiple experiments show that the proposed method significantly improves the docking accuracy of the intelligent wheelchair/bed.

Low-Cost Real-Time Localisation for Agricultural Robots in Unstructured Farm Environments

Agricultural robots have demonstrated significant potential in enhancing farm operational efficiency and reducing manual labour. However, unstructured and complex farm environments present challenges to the precise localisation and navigation of robots in real time. Furthermore, the high costs of navigation systems in agricultural robots hinder their widespread adoption in cost-sensitive agricultural sectors. This study compared two localisation methods that use the Error State Kalman Filter (ESKF) to integrate data from wheel odometry, a low-cost inertial measurement unit (IMU), a low-cost real-time kinematic global navigation satellite system (RTK-GNSS) and the LiDAR-Inertial Odometry via Smoothing and Mapping (LIO-SAM) algorithm using a low-cost IMU and RoboSense 16-channel LiDAR sensor. These two methods were tested on unstructured farm environments for the first time in this study. Experiment results show that the ESKF sensor fusion method without a LiDAR sensor could save 36% of the cost compared to the method that used the LIO-SAM algorithm while maintaining high accuracy for farming applications.

A feature extension and reconstruction method with incremental learning capabilities under limited samples for intelligent diagnosis

Existing data-driven Intelligent Fault Diagnosis (IFD) methods often struggle with continuous learning and integrating new diagnostic knowledge. These methods necessitate a complete retraining process when new fault data emerges. Furthermore, collecting sufficient data for model training is impractical in real-world applications. This study proposes a Feature Extension and Reconstruction (FER) method for model incremental updating with limited samples. FER employs scalable and modular feature extractors for continuous representation learning of new fault modes. In addition, a domain mapping algorithm based on optimal transport is devised to leverage old classification nodes in aiding new nodes’ training, enhancing models’ generalization performance under limited samples. A feature reconstruction technique is also developed to compress the expanded multi-backbone model, eliminating redundant parameters and dimensions for adaptability in subsequent incremental tasks. Validated on simulated transmission systems and real planetary gearboxes, FER demonstrates a better stability-plasticity trade-off in incremental tasks with limited training samples, offering a novel solution for adaptive IFD model updates in sample-constrained scenarios.

Focus-SLAM: A visual Monocular SLAM

Abstract This research presents a novel simultaneous localization and mapping algorithm, called Focus-SLAM, which simulates human navigation strategies by synthesizing a optical saliency model, SalNavNet, in a traditional single-shot SLAM paradigm. SalNavNet introduces an innovative design that incorporates a correlation module and an adaptive Exponential Moving Average (EMA) component, effectively addressing the prevalent center bias issue found in contemporary saliency models. As a result, the system enhances target fixation by accentuating salient features more effectively. Extensive experimental evaluations, conducted across a spectrum of indoor environments exhibiting diverse luminosity conditions. These findings attest to the efficacy of Focus-SLAM in enhancing both the accuracy and efficiency of SLAM operations under challenging real-world conditions.

Improvement of horizontal streak on disparity map thru parameter optimization for stereo vision algorithm

In this paper, an improved local based stereo vision disparity map (SVDM) algorithm is proposed. The proposed local based SVDM algorithm include four stages and they are matching cost computation, cost aggregation disparity optimization and disparity refinement. The matching cost computation started by combining pixel to pixel matching techniques, which are absolute difference (AD) and gradient matching (GM) in producing the initial disparity map. Next, the cost aggregation uses minimum spanning tree (MST) segmentation, which equipped with edge preserving properties and noise filtering. Then, disparity optimization uses local approach with winner-take-all (WTA) technique. At the final stage, disparity refinement uses bilateral filter (BF) with weighted median (WM), which can improve the disparity map through noise removing and edges preserving. Then, the research continues to optimize the proposed local based SVDM algorithm through parameters optimization in obtaining the final disparity map. Here, multiple parameters from the proposed SVDM algorithm are manipulated and they are constant values for GM and several constant parameters in BF. By selecting the optimum parameter values, the performance of the proposed SVDM algorithm increased, especially robustness towards the horizontal streaks.

Fast mapping algorithm with optimized placement selection for CGRA

Fast mapping algorithm with optimized placement selection for CGRA

EcoFlow: Efficient Convolutional Dataflows on Low-Power Neural Network Accelerators

Dilated and transposed convolutions are widely used in modern convolutional neural networks (CNNs). These kernels are used extensively during CNN training and inference of applications such as image segmentation and high-resolution image generation. We find that commonly-used low-power CNN inference accelerators are <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">not</i> optimized for both these convolutional kernels. Dilated and transposed convolutions introduce significant zero padding when mapped to the underlying spatial architecture, significantly degrading performance and energy efficiency. Existing approaches that address this issue require significant design changes to the otherwise simple, efficient, and well-adopted architectures used to compute direct convolutions. To address this challenge, we propose EcoFlow, a new set of dataflows and mapping algorithms for dilated and transposed convolutions. These algorithms are tailored to execute efficiently on existing low-cost, small-scale spatial architectures and requires minimal changes to existing accelerators. At its core, EcoFlow eliminates zero padding through careful dataflow orchestration and data mapping tailored to the spatial architecture. We evaluate EcoFlow on CNN training workloads and Generative Adversarial Network (GAN) workloads. Experiments in our new cycle-accurate simulator show that, using a common CNN inference accelerator, EcoFlow 1) reduces end-to-end CNN training time between 7-85%, and 2) improves end-to-end GAN training performance between 29-42%, compared to state-of-the-art CNN dataflows.

Improved diffusion mapping combined with procrustes analysis for capturing local-global data structures in industrial process monitoring

BackgroundProcess monitoring, by providing early warnings of abnormal operating states resulting from process faults, facilitates the maintenance of normal production and ensures process safety. In the domain of industrial process monitoring, capturing the local-global structural features of data and acquiring an explicit mapping relationship for dimensionality reduction projection holds significant importance for online fault detection in industrial processes. MethodsThis study introduces an Improved Diffusion Mapping and Procrustes analysis (IDM-P) method for this purpose. Initially, considering the multiscale and correlation among industrial data features, the Mahalanobis distance is incorporated to improve the diffusion mapping algorithm. Utilizing this method allows for the concurrent capture of both local and global data structures, leading to a more efficient extraction of data-representative features, which enhances the accuracy of fault detection. Procrustes analysis is then used to obtain an explicit mapping matrix between high-dimensional data and low-dimensional manifolds, improving the efficiency of the key feature extraction of the new samples. Finally, this matrix is utilized to construct process monitoring statistics for fault detection. Significant FindingsThe method's effectiveness was validated through experiments on the TEP dataset and actual industrial data, demonstrating that IDM-P maintains higher accuracy and achieves optimal fault detection compared to other methods.

Hierarchical elastoplasticity of cortical bone: Observations, mathematical modeling, validation

Motivated by the water layer-coated nanoscale bone mineral crystals and the elastoplastic behavior seen at the extracellular scale, we develop a six-step hierarchical micromechanics model for the elastoplasticity of cortical bone. For that purpose, the Eshelby problem-based concentration-influence tensor concept is generalized for a multi-scale situation, quantifying the mechanical interaction between elastic and plastic strains between material phases across six orders of magnitude in observation scale. This hierarchical interaction scheme is complemented by non-associated Mohr–Coulomb plasticity assigned to the mineral crystal phases, and a return-mapping algorithm which adapts classical computational mechanics approaches for the realm of semi-analytical continuum micromechanics. Founded on elastic and strength properties of molecular collagen and hydroxyapatite, the model passes experimental validation against ultrasonic and quasi-static tests at the extrafibrillar, extracellular, extravascular, and cortical observation scales, across different tissues and species. It reveals cortical bone strength to increase nonlinearly with the vascular porosity, and to depend bi-linearly on the extracellular mass density, while elucidating plastic spreading events at the nanocrystal scale, which are fundamentally different in tensile and compressive loading.

W-VSLAM: A Visual Mapping Algorithm for Indoor Inspection Robots.

In recent years, with the widespread application of indoor inspection robots, high-precision, robust environmental perception has become essential for robotic mapping. Addressing the issues of visual-inertial estimation inaccuracies due to redundant pose degrees of freedom and accelerometer drift during the planar motion of mobile robots in indoor environments, we propose a visual SLAM perception method that integrates wheel odometry information. First, the robot's body pose is parameterized in SE(2) and the corresponding camera pose is parameterized in SE(3). On this basis, we derive the visual constraint residuals and their Jacobian matrices for reprojection observations using the camera projection model. We employ the concept of pre-integration to derive pose-constraint residuals and their Jacobian matrices and utilize marginalization theory to derive the relative pose residuals and their Jacobians for loop closure constraints. This approach solves the nonlinear optimization problem to obtain the optimal pose and landmark points of the ground-moving robot. A comparison with the ORBSLAM3 algorithm reveals that, in the recorded indoor environment datasets, the proposed algorithm demonstrates significantly higher perception accuracy, with root mean square error (RMSE) improvements of 89.2% in translation and 98.5% in rotation for absolute trajectory error (ATE). The overall trajectory localization accuracy ranges between 5 and 17 cm, validating the effectiveness of the proposed algorithm. These findings can be applied to preliminary mapping for the autonomous navigation of indoor mobile robots and serve as a basis for path planning based on the mapping results.

Tracking paddy rice acreage, flooding impacts, and mitigations during El Niño flooding events using Sentinel-1/2 imagery and cloud computing

The frequent occurrence of El Niño events, in the context of climate change, brings heavy precipitation and extreme heat, severely disrupting agricultural production. Previous efforts have focused on monitoring crop planting areas and evaluating affected crops during disasters. Nevertheless, a comprehensive analysis, including crop planting area mapping, crop damage assessment, and mitigation effectiveness throughout the entire course of a disaster, has been seldom addressed. In this study, we built a comprehensive framework to rapidly investigate the areas of early rice, the extent of flooding impacts, and the post-flood mitigations of early rice during the El Niño flooding event in a typical rice production region – Jiangxi Province in 2023. Early rice planting areas were first mapped by integrating 15-day time series gap-filled Sentinel-1/2 datasets using the Google Earth Engine (GEE) platform, based on a random forest classifier built with the 55 optimized training features. Then the flood-affected early rice map was produced by integrating the early rice planting areas and the Sentinel-1 images-based flood map. Finally, the post-flood newly planted rice fields were identified using the random forest algorithm and classification features from the Sentinel-1/2 images composited during four phenology phases of newly planted rice. The results showed the early rice planting area map, the flooding map, and the newly planted early rice map have overall accuracies of over 90 %. The early rice planting areas reached 120 × 104 ha, and an area of 3.60 × 104 ha (3 %) was flooded due to the heavy rain, and 3.43 × 104 ha flooded areas were newly planted, eventually mitigating the flooding impacts on the production of early rice. This study showcases the potential of all the available Sentinel-1/2 data, cloud computing, and well-established mapping algorithms for tracking rice areas, flooding impacts, and mitigations (i.e., after-flooding replanting) during extreme climate events. The established framework is expected to serve as an early warning system for agricultural adaptation to extreme climate events.

Extracapsular extension risk assessment using an artificial intelligence prostate cancer mapping algorithm

AbstractObjectiveThe objective of this study is to compare detection rates of extracapsular extension (ECE) of prostate cancer (PCa) using artificial intelligence (AI)‐generated cancer maps versus MRI and conventional nomograms.Materials and methodsWe retrospectively analysed data from 147 patients who received MRI‐targeted biopsy and subsequent radical prostatectomy between September 2016 and May 2022. AI‐based software cleared by the United States Food and Drug Administration (Unfold AI, Avenda Health) was used to map 3D cancer probability and estimate ECE risk. Conventional ECE predictors including MRI Likert scores, capsular contact length of MRI‐visible lesions, PSMA T stage, Partin tables, and the “PRedicting ExtraCapsular Extension” nomogram were used for comparison.Postsurgical specimens were processed using whole‐mount histopathology sectioning, and a genitourinary pathologist assessed each quadrant for ECE presence. ECE predictors were then evaluated on the patient (Unfold AI versus all comparators) and quadrant level (Unfold AI versus MRI Likert score). Receiver operator characteristic curves were generated and compared using DeLong's test.ResultsUnfold AI had a significantly higher area under the curve (AUC = 0.81) than other predictors for patient‐level ECE prediction. Unfold AI achieved 68% sensitivity, 78% specificity, 71% positive predictive value, and 75% negative predictive value. At the quadrant level, Unfold AI exceeded the AUC of MRI Likert scores for posterior (0.89 versus 0.82, p = 0.003), anterior (0.84 versus 0.80, p = 0.34), and all quadrants (0.89 versus 0.82, p = 0.002). The false negative rate of Unfold AI was lower than MRI in both the anterior (−60%) and posterior prostate (−40%).ConclusionsUnfold AI accurately predicted ECE risk, outperforming conventional methodologies. It notably improved ECE prediction over MRI in posterior quadrants, with the potential to inform nerve‐spare technique and prevent positive margins. By enhancing PCa staging and risk stratification, AI‐based cancer mapping may lead to better oncological and functional outcomes for patients.

Color Image Encryption Model Based on 3-D Chaotic Logistic Map and JAYA Algorithm

In this research, we have proposed a color image encryption model based on a three-dimensional chaotic logistic map and the JAYA algorithm. The 3-D chaotic logistic map generates three random keys for the color image. Further, the JAYA algorithm is utilized to find the parameter values of a 3-D chaotic logistic map based on the objective function. The JAYA algorithm is chosen because no parameter tuning is required and it has a better convergence rate to find the best solution over others. After generating the random key, exclusive-OR, and pixel-level bits are shuffled to get the final encrypted image. With the typical collection of color images, the simulation assessment was performed. The outcome indicates that the distorted image is entirely noisy. In comparison to best-practice approaches described in the literature, the suggested model outperforms them in terms of entropy while demonstrating poor PSNR and CC.

Precise management and control around the landfill integrating artificial intelligence and groundwater pollution risks

The Landfill plays an important role in urban development and waste disposal. However, landfill leachate may also bring more serious pollution and health risks to the surrounding groundwater environment. Compared with other areas, the area around the landfill needs more precise management. To solve this problem, based on the “pressure-state-response” framework, a method for the identification and evaluation of groundwater pollution around the landfill was constructed. The LPI method was used to assess the contamination potential of the leachate. The comprehensive quality of groundwater was evaluated by the entropy-AHP water quality assessment method, sodium adsorption ratio and sodium percentage. The probabilistic health risks of groundwater were assessed based on a Monte Carlo algorithm. The sources of pollutants were identified by comprehensively using the PCA-APCS-MLR model and the PMF model. Finally, the self-organizing map algorithm and the Kmeans algorithm were integrated to enhance the precision of groundwater management and control measures. The results showed that the leachate of the landfill was in the mature stage, and the concentration of inorganic substances was relatively high. Leachate had the potential to contaminate surrounding groundwater. The groundwater quality of 68.14% of the study area was in the poor or lower level. The groundwater near the landfill was unsuitable not only for drinking but also for irrigation purposes. Cl− was the main non-carcinogenic risk factor. Reducing pollutant concentration and controlling exposure time are effective strategies for mitigating health risks caused by high-concentration pollutants (Cl−, NO3−) and low-concentration pollutants (F−), respectively. The groundwater around the landfill was jointly affected by six pollution sources. The PMF model has better analytical ability in mixed pollution areas. The groundwater in the study area was divided into five clusters, of which cluster Ⅰ was significantly affected by leachate, and cluster Ⅴ had the lowest pollution and health risk.

Adaptive robot localization in dynamic environments through self-learnt long-term 3D stable points segmentation

In field robotics, particularly in the agricultural sector, precise localization presents a challenge due to the constantly changing nature of the environment. Simultaneous Localization and Mapping algorithms can provide an effective estimation of a robot’s position, but their long-term performance may be impacted by false data associations. Additionally, alternative strategies such as the use of RTK-GPS can also have limitations, such as dependence on external infrastructure. To address these challenges, this paper introduces a novel stability scan filter. This filter can learn and infer the motion status of objects in the environment, allowing it to identify the most stable objects and use them as landmarks for robust robot localization in a continuously changing environment. The proposed method involves an unsupervised point-wise labelling of LiDAR frames by utilizing temporal observations of the environment, as well as a regression network, called Long-Term Stability Network (LTS-NET) to learn and infer 3D LiDAR points long-term motion status. Experiments demonstrate the ability of the stability scan filter to infer the motion stability of objects on a real agricultural long-term dataset. Results show that by only utilizing points belonging to long-term stable objects, the localization system exhibits reliable and robust localization performance for long-term missions compared to using the entire LiDAR frame points.

Novel grayscale image encryption based on 4D fractional-order hyperchaotic system, 2D Henon map and knight tour algorithm

With the increasing frequency of data exchange, the security of transmitted information, especially images, has become paramount. This paper proposes a novel algorithm for encrypting grayscale images of any dimension by using a proposed fractional-order (FO) 4D hyperchaotic system, 2D Henon chaotic map permutation, and the knight tour algorithm. Initially, chaotic sequences are generated by utilizing the proposed FO 4D hyperchaotic system, which are later employed to rearrange and shuffle the entire image pixels to bolster the efficacy of image encryption. To introduce an additional layer of diffusion, 2D Henon chaotic map permutation is used. Furthermore, the knight tour algorithm is applied by starting from a chosen point and executing specified rounds on the scrambled image to increase the encryption’s robustness. The resultant image encryption algorithm undergoes thorough testing and evaluation. It exhibits high sensitivity to the encryption key and boasts a larger key space, rendering it more resistant to brute-force attacks. The proposed algorithm demonstrates an approximate correlation of 0 between adjacent pixels. Further, encryption of a grayscale image of size 256 × 256 takes approximately 0.4 seconds, rendering it more suitable for cryptographic purposes.

Multiscale modeling of diffuse damage and localized cracking in quasi-brittle materials under compression with a quadratic friction law

The diffuse damage and localized cracking of quasi-brittle materials (i.e., rocks and concretes) under compression can be delineated by a matrix-microcrack system, wherein a solid matrix phase is weakened by a large number of randomly oriented and distributed microcracks, and the macroscopic cracking is formed by a progressive evolution of microcracks. Several homogenization-based multiscale models have been proposed to describe this matrix-microcrack system, but most of them are based on a linear friction law on the microcrack surface, rendering a linear strength criterion. In this paper, we propose a new quadratic friction law within the local multiscale friction-damage (LMFD) model to capture the plastic distortion due to frictional sliding along the rough microcrack surface. Following that, a macroscopic Ottosen-type nonlinear strength criterion is rationally derived with up-scaling friction-damage coupling analysis. An enhanced semi-implicit return mapping (ESRM) algorithm with a substepping scheme is then developed to integrate the complex nonlinear constitutive model. The performance of LMFD model is evaluated compared to a wide range of experimental data on plain concretes, and the robustness of ESRM algorithm is assessed through a series of numerical tests. Subsequently, to effectively describe the localized cracking process, a regularization scheme is proposed by combining the phase-field model with the established LMFD model, and the discretization independent crack localization is numerically verified.

Application of texture mapping algorithm in irregular surface art images

The texture mapping technique based on irregular surfaces is widely used in many fields such as film and television, industry and games, etc. In order to adapt to the rapid development in the field of computer graphics and further enhance the uniformity and effectiveness of the texture mapping effect, a triangular mesh simplified texture mapping technique based on the optimized spring-fingertip is proposed. Firstly, a complex two-dimensional graph is established through the spring-fingertip model, which is parameterized and normalized to reduce the deformation of the texture; subsequently, the concept of triangular mesh simplification is introduced to optimize the model timeliness, which replaces the traditional way of folding the edges; finally, the weight of each edge is further analyzed through local curvature calculation. In order to verify the effectiveness of the texture mapping method, simulation and analysis experiments are done, and the experimental results show that the accuracy of the model reaches 99.25%, which is an average improvement of 7.19% relative to the remaining four models. Therefore, the texture mapping model based on optimized triangular mesh effectively improves the realism of the mapping effect and reduces the computational burden of the model.

A Novel Approach for Farmland Size Estimation in Small-Scale Agriculture Using Edge Counting and Remote Sensing

Accurate and timely information on farmland size is crucial for agricultural development, resource management, and other related fields. However, there is currently no mature method for estimating farmland size in smallholder farming areas. This is due to the small size of farmland plots in these areas, which have unclear boundaries in medium and high-resolution satellite imagery, and irregular shapes that make it difficult to extract complete boundaries using morphological rules. Automatic farmland mapping algorithms using remote sensing data also perform poorly in small-scale farming areas. To address this issue, this study proposes a farmland size evaluation index based on edge frequency (ECR). The algorithm utilizes the high temporal resolution of Sentinel-2 satellite imagery to compensate for its spatial resolution limitations. First, all Sentinel-2 images from one year are used to calculate edge frequencies, which can divide farmland areas into low-value farmland interior regions, medium-value non-permanent edges, and high-value permanent edges (PE). Next, the Otsu’s thresholding algorithm is iteratively applied twice to the edge frequencies to first extract edges and then permanent edges. The ratio of PE to cropland (ECR) is then calculated. Using the North China Plain and Northeast China Plain as study areas, and comparing with existing farmland size datasets, the appropriate estimation radius for ECR was determined to be 1600 m. The study found that the peak ECR value for the Northeast China Plain was 0.085, and the peak value for the North China Plain was 0.105. The overall distribution was consistent with the reference dataset.