Intelligent Smart Glass for Visually Impaired Using Deep Learning Machine Vision Techniques and Robot Operating System (ROS)

2018 ◽  
pp. 99-112 ◽  
Author(s):  
Aswath Suresh ◽  
Chetan Arora ◽  
Debrup Laha ◽  
Dhruv Gaba ◽  
Siddhant Bhambri
Keyword(s):  
Operating System ◽  
Deep Learning ◽  
Machine Vision ◽  
Visually Impaired ◽  
Robot Operating System ◽  
Learning Machine

scholarly journals Chemical crystal identification with deep learning machine vision

2018 ◽  
Vol 11 (1) ◽  
Author(s):  
Perseverança Mungofa ◽  
Arnold Schumann ◽  
Laura Waldo
Keyword(s):  
Deep Learning ◽  
Machine Vision ◽  
Learning Machine

scholarly journals On machine vision and photographic imagination

AI & Society ◽  
2020 ◽  
Author(s):  
Daniel Chávez Heras ◽  
Tobias Blanke
Keyword(s):  
Computer Vision ◽  
Deep Learning ◽  
Machine Vision ◽  
Vision Systems ◽  
Computational Aesthetics ◽  
Television Programme ◽  
Computer Vision Systems ◽  
Learning Machine

Abstract In this article we introduce the concept of implied optical perspective in deep learning computer vision systems. Taking the BBC's experimental television programme “Made by Machine: When AI met the Archive” (2018) as a case study, we trace a conceptual and material link between the system used to automatically “watch” the television archive and a specific type of photographic practice. From a computational aesthetics perspective, we show how deep learning machine vision relies on photography, its technical regimes and epistemic advantages, and we propose a novel way to identify the latent camera through which the BBC archive was seen by machine.

F1_Tenth_Course_Report_Group_C

2018 ◽  
Author(s):  
Yi Chen ◽  
Sagar Manglani ◽  
Roberto Merco ◽  
Drew Bolduc
Keyword(s):  
Operating System ◽  
Autonomous Driving ◽  
Software Tools ◽  
Simulation Environment ◽  
Robot Operating System ◽  
Available Information

In this paper, we discuss several of major robot/vehicle platforms available and demonstrate the implementation of autonomous techniques on one such platform, the F1/10. Robot Operating System was chosen for its existing collection of software tools, libraries, and simulation environment. We build on the available information for the F1/10 vehicle and illustrate key tools that will help achieve properly functioning hardware. We provide methods to build algorithms and give examples of deploying these algorithms to complete autonomous driving tasks and build 2D maps using SLAM. Finally, we discuss the results of our findings and how they can be improved.

Classification and Recognition of Electronic Components Based on Improved Cooperative Semi-supervised Learning Algorithm

2020 ◽  
Vol 14 ◽  
Author(s):  
Dan Luo
Keyword(s):  
Deep Learning ◽  
Machine Vision ◽  
Supervised Learning ◽  
Image Recognition ◽  
Production Efficiency ◽  
Learning Algorithm ◽  
Electronic Components ◽  
Electron Device ◽  
Actual Recognition ◽  
The Subject

Background: As known that the semi-supervised algorithm is a classical algorithm in semi-supervised learning algorithm. Methods: In the paper, it proposed improved cooperative semi-supervised learning algorithm, and the algorithm process is presented in detailed, and it is adopted to predict unlabeled electronic components image. Results: In the experiments of classification and recognition of electronic components, it show that through the method the accuracy the proposed algorithm in electron device image recognition can be significantly improved, the improved algorithm can be used in the actual recognition process . Conclusion: With the continuous development of science and technology, machine vision and deep learning will play a more important role in people's life in the future. The subject research based on the identification of the number of components is bound to develop towards the direction of high precision and multi-dimension, which will greatly improve the production efficiency of electronic components industry.

scholarly journals A Robot Operating System Framework for Secure UAV Communications

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1369
Author(s):  
Hyojun Lee ◽  
Jiyoung Yoon ◽  
Min-Seong Jang ◽  
Kyung-Joon Park
Keyword(s):  
Operating System ◽  
Unmanned Aerial Vehicles ◽  
Ground Control ◽  
Unmanned Aerial System ◽  
Security Framework ◽  
Security Vulnerabilities ◽  
Robot Operating System ◽  
Security Issues ◽  
Aerial Vehicles ◽  
Ground Control Station

To perform advanced operations with unmanned aerial vehicles (UAVs), it is crucial that components other than the existing ones such as flight controller, network devices, and ground control station (GCS) are also used. The inevitable addition of hardware and software to accomplish UAV operations may lead to security vulnerabilities through various vectors. Hence, we propose a security framework in this study to improve the security of an unmanned aerial system (UAS). The proposed framework operates in the robot operating system (ROS) and is designed to focus on several perspectives, such as overhead arising from additional security elements and security issues essential for flight missions. The UAS is operated in a nonnative and native ROS environment. The performance of the proposed framework in both environments is verified through experiments.

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