1,720,954 research outputs found
A NOVEL DATA FUSION FRAMEWORK TO ENHANCE CONTEXTUAL AWARENESS OF THE AUTONOMOUS VEHICLES FOR ACCURATE DECISION MAKING
Get PDFAutonomous driving has the potential to bring significant changes and benefits to various aspects of transportation. Autonomous vehicles (AVs) use a combination of advanced sensors, cameras, radar, lidar, GPS, maps, and AI algorithms to perceive their environment, make decisions, and control their movements. Though there is a significant increase in the AVs utility, there are several challenges associated with the AVs among which ensuring safety and security for a reliable drive is still an existing challenge. The majority of accidents involving the AVs result from faulty decision-making resulting in fatal incidents. Multiple elements contribute to the flawed decision-making in autonomous vehicles (AVs), with inaccurate context creation being highlighted as one of the pivotal factors. For comprehensive safety across diverse driving environments, an autonomous vehicle must adeptly and dependably interpret its surroundings. Inadequate data acquisition from diverse sources and insufficient data pre-processing are the primary factors contributing to this inaccurate formation of context. To enhance environmental awareness and boost decision-making precision in autonomous vehicles (AVs), a versatile framework has been proposed. This framework incorporates multiple modules designed to oversee vital tasks such as collecting and organizing sensory data in diverse formats, extracting pertinent features, fusing them effectively, establishing precise context, and creating inventive and rapid decision protocols for timely decision-making in AVs. This research introduces innovative mechanisms for sensory data classification and versatile machine learning (ML) models for feature extraction and data fusion. A novel mechanism is proposed for instant rule framing and decision-making based on the fused data. This endeavour is commenced by presenting an outline of the functionality of the proposed framework, specifically highlighting image and video data formats, which hold prominence in sensory data. Efficient models have been suggested with the aim of extracting vital image attributes, including edges, color, height, and width. Furthermore, an ingenious mathematical model is introduced, utilizing advanced matrix transformations and progressive modes to convert two-dimensional image data formats into three-dimensional ones. This mathematical model serves as the fundamental kernel function for the proposed Convolutional Neural Network (CNN) model, enabling the fusion of different image data formats. Additional innovative concepts and mechanisms are introduced in this research to enhance the performance of the proposed models. The extension of the proposed edge detection model encompasses detecting edges in all directions within the input image, surpassing the previous limitation of solely identifying horizontal and vertical edges. Advanced mathematical models incorporating genetic mutation techniques are proposed to accomplish this task. Versatile kernel functions are developed to process 3D point cloud sensory data, which are integrated into the proposed Generative Adversarial Network (GAN) model for classifying and fusing different image data formats. The extended research effectively completes the functionalities of the remaining modules within the proposed framework. In the expanded work, novel models have been introduced to efficiently combine textual and audio data. Additionally, versatile models for object detection and classification have been presented, enhancing the accurate recognition and categorization of objects. Advanced techniques such as ensembling, gating, and filtering are incorporated to select the most suitable object detection and classification model. Further, innovative methodologies are proposed to establish accurate context and decision rules. The performance evaluation of the proposed models utilizes widely recognized datasets namely KITTI, nuScenes, RADIATE, OSU, BPEM and GeoTiles. The suggested image fusion model achieved an accuracy of 98% and demonstrated a faster execution time (0.98s) compared to other well-known image fusion models. Alternatively, the suggested object detection model demonstrated a sensitivity of 0.65 and an average precision of 0.85, confirming its improved performance than other widely acknowledged object detection models, but. Further results are discussed in the Experimental Analysis portion of Chapter 3, which is the outcome of extensive investigations carried out on several parameters to evaluate the suggested models
A NOVEL DATA FUSION FR AMEWORK TO ENHANCE CONTEXTUAL AWARENESS OF THE AUTONOMOUS VEHICLES FOR ACCURATE DECISION MAKING
Get PDFAutonomous driving has the potential to bring significant changes and benefits to various aspects of transportation. Autonomous vehicles (AVs) use a combination of advanced sensors, cameras, radar, lidar, GPS, maps, and AI algorithms to perceive their environment, make decisions, and control their movements. Though there is a significant increase in the AVs utility, there are several challenges associated with the AVs among which ensuring safety and security for a reliable drive is still an existing challenge. The majority of accidents involving the AVs result from faulty decision-making resulting in fatal incidents. Multiple elements contribute to the flawed decision-making in autonomous vehicles (AVs), with inaccurate context creation being highlighted as one of the pivotal factors. For comprehensive safety across diverse driving environments, an autonomous vehicle must adeptly and dependably interpret its surroundings. Inadequate data acquisition from diverse sources and insufficient data pre-processing are the primary factors contributing to this inaccurate formation of context. To enhance environmental awareness and boost decision-making precision in autonomous vehicles (AVs), a versatile framework has been proposed. This framework incorporates multiple modules designed to oversee vital tasks such as collecting and organizing sensory data in diverse formats, extracting pertinent features, fusing them effectively, establishing precise context, and creating inventive and rapid decision protocols for timely decision-making in AVs. This research introduces innovative mechanisms for sensory data classification and versatile machine learning (ML) models for feature extraction and data fusion. A novel mechanism is proposed for instant rule framing and decision-making based on the fused data. This endeavour is commenced by presenting an outline of the functionality of the proposed framework, specifically highlighting image and video data formats, which hold prominence in sensory data. Efficient models have been suggested with the aim of extracting vital image attributes, including edges, colour, height, and width. Furthermore, an ingenious mathematical model is introduced, utilizing advanced matrix transformations and progressive modes to convert two-dimensional image data formats into three-dimensional ones. This mathematical model serves as the fundamental kernel function for the proposed Convolutional Neural Network (CNN) model, enabling the fusion of different image data formats. Additional innovative concepts and mechanisms are introduced in this research to enhance the performance of the proposed models. The extension of the proposed edge detection model encompasses detecting edges in all directions within the input image, surpassing the previous limitation of solely identifying horizontal and vertical edges. Advanced mathematical models incorporating genetic mutation techniques are proposed to accomplish this task. Versatile kernel functions are developed to process 3D point cloud sensory data, which are integrated into the proposed Generative Adversarial Network (GAN) model for classifying and fusing different image data formats. The extended research effectively completes the functionalities of the remaining modules within the proposed framework. In the expanded work, novel models have been introduced to efficiently combine textual and audio data. Additionally, versatile models for object detection and classification have been presented, enhancing the accurate recognition and categorization of objects. Advanced techniques such as ensembling, gating, and filtering are incorporated to select the most suitable object detection and classification model. Further, innovative methodologies are proposed to establish accurate context and decision rules. The performance evaluation of the proposed models utilizes widely recognized datasets namely KITTI, nuScenes, RADIATE, OSU, BPEM and GeoTiles. The suggested image fusion model achieved an accuracy of 98% and demonstrated a faster execution time (0.98s) compared to other well-known image fusion models. Alternatively, the suggested object detection model demonstrated a sensitivity of 0.65 and an average precision of 0.85, confirming its improved performance than other widely acknowledged object detection models. Further results are discussed in the Experimental Analysis portion of Chapter 5, which is the outcome of extensive investigations carried out on several parameters to evaluate the suggested models
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Get PDFWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
koamabayili/VECTRON-author-checklist: VECTRON author checklist
No full textWe have done our best to complete the author checklist relating to the use of animals in the hut study. Note that the objective for the hut study was to evaluate the IRS treatment applications for residual efficacy against Anopheles mosquitoes, including the local An. coluzzii mosquito population. Cows were only used to attract mosquitoes into the huts and no tests were carried out directly on the cows. The author checklist is intended for use with studies where experiments are carried out on animals, which is why we have had such difficulty in completing this for the hut study, as many of the questions do not relate to how the cows were used
Author-wise bibliometric analysis based on entropy.
No full textAuthor-wise bibliometric analysis based on entropy.</p
- …
