Search results

This study aims to introduce a novel noise filter module designed for LiDAR simultaneous localization and mapping (SLAM) systems. The primary objective is to enhance pose estimation accuracy and improve the overall system performance in outdoor environments.

Distinct from traditional approaches, MCFilter emphasizes enhancing point cloud data quality at the pixel level. This framework hinges on two primary elements. First, the D-Tracker, a tracking algorithm, is grounded on multiresolution three-dimensional (3D) descriptors and adeptly maintains a balance between precision and efficiency. Second, the R-Filter introduces a pixel-level attribute named motion-correlation, which effectively identifies and removes dynamic points. Furthermore, designed as a modular component, MCFilter ensures seamless integration into existing LiDAR SLAM systems.

Based on rigorous testing with public data sets and real-world conditions, the MCFilter reported an increase in average accuracy of 12.39% and reduced processing time by 24.18%. These outcomes emphasize the method’s effectiveness in refining the performance of current LiDAR SLAM systems.

In this study, the authors present a novel 3D descriptor tracker designed for consistent feature point matching across successive frames. The authors also propose an innovative attribute to detect and eliminate noise points. Experimental results demonstrate that integrating this method into existing LiDAR SLAM systems yields state-of-the-art performance.

This study aims to learn the dynamic radar cross-section (RCS) of a deflection air brake.

The aircraft model with delta wing, V-shaped tail and blended wing body is designed, and high-precision unstructured grid technology is used to deal with the surface of air brake and fuselage. The calculation method based on multiple tracking and dynamic scattering is presented to calculate RCS.

The fuselage has a low scattering level, and the opening air brake will bring obvious dynamic RCS effects to itself and the whole machine. The average indicator of air brake RCS can be lower than –0.6 dBm² under the tail azimuth, while that of forward and lateral direction is lower. The mean RCS of fuselage is obviously higher than that of air brake, while the deflected air brake and its cabin can still provide strong scattering sources at some azimuths. When the air brake is opening, the change amplitude of the aircraft forward RCS can exceed 19.81 dBm².

This research has practical significance for the dynamic electromagnetic scattering analysis and stealth design of the air brake.

The calculation method for aircraft RCS considering air brake dynamic deflection has been established.

This study aims to perform three-dimensional numerical computations for blood flow through a double stenosed carotid artery. Pulsatile flow with Womersley number (Wo) of 4.65 and Reynolds number (Re) of 425, based on the diameter of normal artery and average velocity of inlet pulse, was considered.

Finite volume method based ANSYS Fluent 20.1 was used for solving the governing equations of three-dimensional, laminar, incompressible and non-Newtonian blood flow. A high-quality grid with sufficient refinement was generated using ICEM CFD 20.1. The time-averaged flow field was captured to investigate the effect of severity and eccentricity on the lumen flow characteristics.

The results show that an increase in interspacing between blockages brings shear layer instability within the region between two blockages. The velocity profile and wall shear stress distribution are found to be majorly influenced by eccentricity. On the other hand, their peak magnitude is found to be primarily influenced by severity. Results have also demonstrated that the presence of eccentricity in stenosis would assist in flow development.

Variation in severity and interspacing was considered with a provision of eccentricity equal to 10% of diameter. Eccentricity refers to the offset between the centreline of stenosis and the centreline of normal artery. For the two blockages, severity values of 40% and 60% based on diameter reduction were permuted, giving rise to four combinations. For each combination, three values of interspacing in the multiples of normal artery diameter (D), viz. 4D, 6D and 8D were considered.

With the help of basic physics, the application of computer algorithms in the form of recent advances such as machine learning and neural networking in textile Industry has been discussed in this review. Scientists have linked the underlying structural or chemical science of textile materials and discovered several strategies for completing some of the most time-consuming tasks with ease and precision. Since the 1980s, computer algorithms and machine learning have been used to aid the majority of the textile testing process. With the rise in demand for automation, deep learning, and neural networks, these two now handle the majority of testing and quality control operations in the form of image processing.

The state-of-the-art of artificial intelligence (AI) applications in the textile sector is reviewed in this paper. Based on several research problems and AI-based methods, the current literature is evaluated. The research issues are categorized into three categories based on the operation processes of the textile industry, including yarn manufacturing, fabric manufacture and coloration.

AI-assisted automation has improved not only machine efficiency but also overall industry operations. AI's fundamental concepts have been examined for real-world challenges. Several scientists conducted the majority of the case studies, and they confirmed that image analysis, backpropagation and neural networking may be specifically used as testing techniques in textile material testing. AI can be used to automate processes in various circumstances.

This research conducts a thorough analysis of artificial neural network applications in the textile sector.

Web users rely heavily on online content make decisions without assessing the veracity of the content. The online content comprising text, image, video or audio may be tampered with to influence public opinion. Since the consumers of online information (misinformation) tend to trust the content when the image(s) supplement the text, image manipulation software is increasingly being used to forge the images. To address the crucial problem of image manipulation, this study focusses on developing a deep-learning-based image forgery detection framework.

The proposed deep-learning-based framework aims to detect images forged using copy-move and splicing techniques. The image transformation technique aids the identification of relevant features for the network to train effectively. After that, the pre-trained customized convolutional neural network is used to train on the public benchmark datasets, and the performance is evaluated on the test dataset using various parameters.

The comparative analysis of image transformation techniques and experiments conducted on benchmark datasets from a variety of socio-cultural domains establishes the effectiveness and viability of the proposed framework. These findings affirm the potential applicability of proposed framework in real-time image forgery detection.

This study bears implications for several important aspects of research on image forgery detection. First this research adds to recent discussion on feature extraction and learning for image forgery detection. While prior research on image forgery detection, hand-crafted the features, the proposed solution contributes to stream of literature that automatically learns the features and classify the images. Second, this research contributes to ongoing effort in curtailing the spread of misinformation using images. The extant literature on spread of misinformation has prominently focussed on textual data shared over social media platforms. The study addresses the call for greater emphasis on the development of robust image transformation techniques.

This study carries important practical implications for various domains such as forensic sciences, media and journalism where image data is increasingly being used to make inferences. The integration of image forgery detection tools can be helpful in determining the credibility of the article or post before it is shared over the Internet. The content shared over the Internet by the users has become an important component of news reporting. The framework proposed in this paper can be further extended and trained on more annotated real-world data so as to function as a tool for fact-checkers.

In the current scenario wherein most of the image forgery detection studies attempt to assess whether the image is real or forged in an offline mode, it is crucial to identify any trending or potential forged image as early as possible. By learning from historical data, the proposed framework can aid in early prediction of forged images to detect the newly emerging forged images even before they occur. In summary, the proposed framework has a potential to mitigate physical spreading and psychological impact of forged images on social media.

This study focusses on copy-move and splicing techniques while integrating transfer learning concepts to classify forged images with high accuracy. The synergistic use of hitherto little explored image transformation techniques and customized convolutional neural network helps design a robust image forgery detection framework. Experiments and findings establish that the proposed framework accurately classifies forged images, thus mitigating the negative socio-cultural spread of misinformation.

The paper aims to solve the problem of personnel intrusion identification within the limits of high-speed railways. It adopts the fusion method of millimeter wave radar and camera to improve the accuracy of object recognition in dark and harsh weather conditions.

This paper adopts the fusion strategy of radar and camera linkage to achieve focus amplification of long-distance targets and solves the problem of low illumination by laser light filling of the focus point. In order to improve the recognition effect, this paper adopts the YOLOv8 algorithm for multi-scale target recognition. In addition, for the image distortion caused by bad weather, this paper proposes a linkage and tracking fusion strategy to output the correct alarm results.

Simulated intrusion tests show that the proposed method can effectively detect human intrusion within 0–200 m during the day and night in sunny weather and can achieve more than 80% recognition accuracy for extreme severe weather conditions.

(1) The authors propose a personnel intrusion monitoring scheme based on the fusion of millimeter wave radar and camera, achieving all-weather intrusion monitoring; (2) The authors propose a new multi-level fusion algorithm based on linkage and tracking to achieve intrusion target monitoring under adverse weather conditions; (3) The authors have conducted a large number of innovative simulation experiments to verify the effectiveness of the method proposed in this article.

This study aims to analyze how conventional Bedouin weaving techniques have changed through the history of Israel, offering knowledge on the craft’s cultural relevance and historical development among the Bedouin people and how their weaving and embroidery differ based on the three main geographic characteristics. It tries to comprehend the causes of the transition from organic to synthetic materials and the part played by the Lakiya Negev Bedouin Weaving women’s cooperative in maintaining this legacy.

The main goal of this study is to trace the emergence of Bedouin weaving traditions in the Negev Desert using a qualitative research methodology that combines historical analysis and ethnographic investigation. A thorough grasp of the subject’s significance is provided through the data gathering, which consists of interviews, archival research and field observations.

Through the years, Bedouin weaving techniques have significantly shifted away from using traditional organic materials in favor of synthetic replacements, according to the research. It emphasizes the crucial part played by the Lakiya Negev Bedouin Weaving women’s organization in safeguarding this traditional legacy and giving Bedouin women access to economic prospects.

The limitation of the study includes its emphasis on the Negev region and the Israeli Bedouin community, which may not accurately reflect all Bedouin weaving techniques. Greater regional settings may be explored in future studies.

The investigation emphasizes the value of investing in initiatives for cultural preservation and the empowerment of underprivileged groups through economic possibilities.

By preserving ancient weaving techniques, this research enables Bedouin women in the Negev Desert to maintain their cultural identity and socioeconomic well-being.

By emphasizing the socio-cultural dimensions and the organization’s role in preserving traditional craftsmanship in a changing socio-economic environment, this research presents a unique investigation of the evolution of Bedouin weaving techniques in Israel.

The main purpose of this study was to prepare the cotton fibers and cellulose powder by a layer of nano-crystalline-titanium dioxide (TiO₂) using the sol-gel sono-synthesis method to clean the wastewater containing reactive dye. Moreover, TiO₂ nano-materials are remarkable due to their photoactive properties and valuable applications in wastewater treatment.

In this research, TiO₂ was synthesized and deposited effectively on cotton fibers and cellulose powder using ultrasound-assisted coating. Further, tetra butyl titanate was used as a precursor to the synthesis of TiO₂ nanoparticles. Reactive dye (red 195) was used in this study. X-ray Diffraction, scanning electron microscopy and Fourier transform infrared spectroscopy were performed to prove the aptitude for the formation of crystal TiO2 on the cotton fibers and cellulose powder along with TiO₂ nanoparticles as well as to analyze the chemical structure. Decoloration of the wastewater was investigated through ultraviolet (UV-Visible) light at 30 min.

The experimental results revealed that the decolorization was completed at 2.0 min with the cellulose nano TiO₂ treatment whereas cotton nano TiO₂ treated solution contained reactive dyestuffs even after the treatment of 2 min. This was the fastest method up to now than all reported methods for sustainable decolorization of wastewater by absorption. Furthermore, this study explored that the cellulose TiO₂ nano-composite was more effective than the cotton TiO₂ nano-composite of decoloration wastewater for the eco-friendly remedy.

Cotton fibers and cellulose powder with nano-TiO₂, and only reactive dye (red 195) were tested.

With reactive dye-containing wastewater, it seems to be easier to get rid of the dye than to retain it, especially from dyeing of yarn, fabric, apparel, and as well as other sectors where dyestuffs are used.

This research would help to reduce pollution in the environment as well as save energy and cost.

Decoloration of wastewater treatment is an essential new track with nano-crystalline TiO2 to fast and efficient cleaning of reactive dyes containing wastewater used as a raw material.

Quality assessment of textile products is of prime concern to intimately meet consumer demands. The dilemma faced by textile producers is to figure out the stability among quality criteria and efficiently deal with target specifications. Hence, the basic devotion is to attain the optimum value product which entirely satisfies the views and perceptions of consumers. Selection of best fabric among several alternatives in the presence of contradictory measures is a disputing problem in multicriteria decision-making.

In the current study, the analytic hierarchy process (AHP) and preference ranking organization method for enrichment evaluation (PROMETHEE) are proficiently used to solve the problem in selection of branded woven shawls. AHP method verifies comparative weights of the criteria selection, while the ranking of fabric alternatives grounded on specific net-outranking flows is executed through PROMETHEE II method.

The collective AHP and PROMETHEE approaches are applied for the useful accomplishment of grading of branded shawls based on multicriteria weights, used for effective selection of fabric materials in the textile market.

In the apparel industry, fabric and garment manufacturers often rely on hit-and-trial methods, leading to significant wastage of valuable resources and time, in achieving the desirable fabric qualities. The implementation of the findings can assist apparel manufacturers in streamlining their fabric selection processes based on multiple criteria. By adopting this method, industry players can make informed decisions, ensuring a balance between quality standards and consumer expectations, thereby enhancing both product value and market competitiveness.

The methods of Visual PROMETHEE and AHP are assimilated to offer a complete method for the selection and grading of fabrics with reference to multiple selection criteria.

To ensure the motion attitude and stable contact force of massage robot working on unknown human tissue environment, this study aims to propose a robotic system for autonomous massage path planning and stable interaction control.

First, back region extraction and acupoint recognition based on deep learning is proposed, which provides a basis for determining the working area and path points of the robot. Second, to realize the standard approach and movement trajectory of the expert massage, 3D reconstruction and path planning of the massage area are performed, and normal vectors are calculated to control the normal orientation of robot-end. Finally, to cope with the soft and hard changes of human tissue state and body movement, an adaptive force tracking control strategy is presented to compensate the uncertainty of environmental position and tissue hardness online.

Improved network model can accomplish the acupoint recognition task with a large accuracy and integrate the point cloud to generate massage trajectories adapted to the shape of the human body. Experimental results show that the adaptive force tracking control can obtain a relatively smooth force, and the error is basically within ± 0.2 N during the online experiment.

This paper incorporates deep learning, 3D reconstruction and impedance control, the robot can understand the shape features of the massage area and adapt its planning massage path to carry out a stable and safe force tracking control during dynamic robot–human contact.