scholarly journals Rock Segmentation in the Navigation Vision of the Planetary Rovers

Mathematics ◽  
2021 ◽  
Vol 9 (23) ◽  
pp. 3048
Author(s):  
Boyu Kuang ◽  
Mariusz Wisniewski ◽  
Zeeshan A. Rana ◽  
Yifan Zhao
Keyword(s):  
Real Time ◽  
Mean Squared Error ◽  
State Of The Art ◽  
Real Life ◽  
Visual Navigation ◽  
The State ◽  
Training Process ◽  
Planetary Rover ◽  
Transfer Training ◽  
Segmentation Framework

Visual navigation is an essential part of planetary rover autonomy. Rock segmentation emerged as an important interdisciplinary topic among image processing, robotics, and mathematical modeling. Rock segmentation is a challenging topic for rover autonomy because of the high computational consumption, real-time requirement, and annotation difficulty. This research proposes a rock segmentation framework and a rock segmentation network (NI-U-Net++) to aid with the visual navigation of rovers. The framework consists of two stages: the pre-training process and the transfer-training process. The pre-training process applies the synthetic algorithm to generate the synthetic images; then, it uses the generated images to pre-train NI-U-Net++. The synthetic algorithm increases the size of the image dataset and provides pixel-level masks—both of which are challenges with machine learning tasks. The pre-training process accomplishes the state-of-the-art compared with the related studies, which achieved an accuracy, intersection over union (IoU), Dice score, and root mean squared error (RMSE) of 99.41%, 0.8991, 0.9459, and 0.0775, respectively. The transfer-training process fine-tunes the pre-trained NI-U-Net++ using the real-life images, which achieved an accuracy, IoU, Dice score, and RMSE of 99.58%, 0.7476, 0.8556, and 0.0557, respectively. Finally, the transfer-trained NI-U-Net++ is integrated into a planetary rover navigation vision and achieves a real-time performance of 32.57 frames per second (or the inference time is 0.0307 s per frame). The framework only manually annotates about 8% (183 images) of the 2250 images in the navigation vision, which is a labor-saving solution for rock segmentation tasks. The proposed rock segmentation framework and NI-U-Net++ improve the performance of the state-of-the-art models. The synthetic algorithm improves the process of creating valid data for the challenge of rock segmentation. All source codes, datasets, and trained models of this research are openly available in Cranfield Online Research Data (CORD).

Smartphone-Based Indoor Visual Navigation with Leader-Follower Mode

2021 ◽  
Vol 17 (2) ◽  
pp. 1-22
Author(s):  
Jingao Xu ◽  
Erqun Dong ◽  
Qiang Ma ◽  
Chenshu Wu ◽  
Zheng Yang
Keyword(s):  
Real Time ◽  
Environmental Changes ◽  
State Of The Art ◽  
Visual Navigation ◽  
Indoor Navigation ◽  
Location Services ◽  
Localization And Mapping ◽  
Leaders And Followers ◽  
Indoor Navigation System ◽  
Free Pair

Existing indoor navigation solutions usually require pre-deployed comprehensive location services with precise indoor maps and, more importantly, all rely on dedicatedly installed or existing infrastructure. In this article, we present Pair-Navi, an infrastructure-free indoor navigation system that circumvents all these requirements by reusing a previous traveler’s (i.e., leader) trace experience to navigate future users (i.e., followers) in a Peer-to-Peer mode. Our system leverages the advances of visual simultaneous localization and mapping ( SLAM ) on commercial smartphones. Visual SLAM systems, however, are vulnerable to environmental dynamics in the precision and robustness and involve intensive computation that prohibits real-time applications. To combat environmental changes, we propose to cull non-rigid contexts and keep only the static and rigid contents in use. To enable real-time navigation on mobiles, we decouple and reorganize the highly coupled SLAM modules for leaders and followers. We implement Pair-Navi on commodity smartphones and validate its performance in three diverse buildings and two standard datasets (TUM and KITTI). Our results show that Pair-Navi achieves an immediate navigation success rate of 98.6%, which maintains as 83.4% even after 2 weeks since the leaders’ traces were collected, outperforming the state-of-the-art solutions by >50%. Being truly infrastructure-free, Pair-Navi sheds lights on practical indoor navigations for mobile users.

A Survey of Real-Time Industrial Wireless LAN Research

2015 ◽  
Vol 738-739 ◽  
pp. 1105-1110 ◽  
Author(s):  
Yuan Qing Qin ◽  
Ying Jie Cheng ◽  
Chun Jie Zhou
Keyword(s):  
Real Time ◽  
Future Development ◽  
Wireless Lan ◽  
State Of The Art ◽  
The State ◽  
Local Networks ◽  
The Real ◽  
Time Requirements ◽  
And Performance ◽  
Fundamental Mechanism

This paper mainly surveys the state-of-the-art on real-time communicaton in industrial wireless local networks(WLANs), and also identifys the suitable approaches to deal with the real-time requirements in future. Firstly, this paper summarizes the features of industrial WLANs and the challenges it encounters. Then according to the real-time problems of industrial WLAN, the fundamental mechanism of each recent representative resolution is analyzed in detail. Meanwhile, the characteristics and performance of these resolutions are adequately compared. Finally, this paper concludes the current of the research and discusses the future development of industrial WLANs.

scholarly journals A Network Parameter Database False Data Injection Correction Physics-Based Model: A Machine Learning Synthetic Measurement-Based Approach

2021 ◽  
Vol 11 (17) ◽  
pp. 8074
Author(s):  
Tierui Zou ◽  
Nader Aljohani ◽  
Keerthiraj Nagaraj ◽  
Sheng Zou ◽  
Cody Ruben ◽  
...  
Keyword(s):  
Machine Learning ◽  
Power Systems ◽  
State Of The Art ◽  
Real Life ◽  
The State ◽  
Wide Area ◽  
Network Parameter ◽  
False Data Injection ◽  
Network Parameters ◽  
Injection Attacks

Concerning power systems, real-time monitoring of cyber–physical security, false data injection attacks on wide-area measurements are of major concern. However, the database of the network parameters is just as crucial to the state estimation process. Maintaining the accuracy of the system model is the other part of the equation, since almost all applications in power systems heavily depend on the state estimator outputs. While much effort has been given to measurements of false data injection attacks, seldom reported work is found on the broad theme of false data injection on the database of network parameters. State-of-the-art physics-based model solutions correct false data injection on network parameter database considering only available wide-area measurements. In addition, deterministic models are used for correction. In this paper, an overdetermined physics-based parameter false data injection correction model is presented. The overdetermined model uses a parameter database correction Jacobian matrix and a Taylor series expansion approximation. The method further applies the concept of synthetic measurements, which refers to measurements that do not exist in the real-life system. A machine learning linear regression-based model for measurement prediction is integrated in the framework through deriving weights for synthetic measurements creation. Validation of the presented model is performed on the IEEE 118-bus system. Numerical results show that the approximation error is lower than the state-of-the-art, while providing robustness to the correction process. Easy-to-implement model on the classical weighted-least-squares solution, highlights real-life implementation potential aspects.

The State of the Art in Real-Time Range Mapping — A Panel Discussion

1990 ◽  
pp. 341-346
Author(s):  
Joseph Wilder ◽  
J.K. Aggarwal ◽  
P. Besl ◽  
T. Kanade ◽  
A. Slotwinski ◽  
...  
Keyword(s):  
Real Time ◽  
State Of The Art ◽  
Panel Discussion ◽  
The State ◽  
Time Range

A Study of the State of the Art in Synthetic Emotional Intelligence in Affective Computing

2020 ◽  
pp. 1199-1212
Author(s):  
Syeda Erfana Zohora ◽  
A. M. Khan ◽  
Arvind K. Srivastava ◽  
Nhu Gia Nguyen ◽  
Nilanjan Dey
Keyword(s):  
Emotional Intelligence ◽  
Affective Computing ◽  
Ethical Issues ◽  
State Of The Art ◽  
Real Life ◽  
Life Time ◽  
The State ◽  
Point Of View ◽  
General Idea ◽  
Tremendous Amount

In the last few decades there has been a tremendous amount of research on synthetic emotional intelligence related to affective computing that has significantly advanced from the technological point of view that refers to academic studies, systematic learning and developing knowledge and affective technology to a extensive area of real life time systems coupled with their applications. The objective of this paper is to present a general idea on the area of emotional intelligence in affective computing. The overview of the state of the art in emotional intelligence comprises of basic definitions and terminology, a study of current technological scenario. The paper also proposes research activities with a detailed study of ethical issues, challenges with importance on affective computing. Lastly, we present a broad area of applications such as interactive learning emotional systems, modeling emotional agents with an intention of employing these agents in human computer interactions as well as in education.

scholarly journals Real-Time Docking Ring Detection Based on the Geometrical Shape for an On-Orbit Spacecraft

Sensors ◽  
2019 ◽  
Vol 19 (23) ◽  
pp. 5243
Author(s):  
Zhang ◽  
Pan ◽  
Ma
Keyword(s):  
Least Squares ◽  
Real Time ◽  
Detection Method ◽  
State Of The Art ◽  
The State ◽  
Selection Strategy ◽  
Geometrical Shape ◽  
Automatic Capture ◽  
Rendezvous And Docking ◽  
Real Time Application

Docking ring is a circular hatch of spacecraft that allows servicing spacecraft to dock in various space missions. The detection of the ring is greatly beneficial to automatic capture, rendezvous and docking. Based on its geometrical shape, we propose a real-time docking ring detection method for on-orbit spacecraft. Firstly, we extract arcs from the edge mask and classify them into four classes according to edge direction and convexity. By developing the arc selection strategy, we select a combination of arcs possibly belonging to the same ellipse, and then estimate its parameters via the least squares fitting technique. Candidate ellipses are validated according to the fitness of the estimation with the actual edge pixels. The experiments show that our method is superior to the state-of-the-art methods, and can be used in real time application. The method can also be extended to other applications.

scholarly journals Multifeature Extreme Ordinal Ranking Machine for Facial Age Estimation

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Wei Zhao ◽  
Han Wang ◽  
Guang-Bin Huang
Keyword(s):  
Age Estimation ◽  
Optimization Problems ◽  
State Of The Art ◽  
Mathematical Optimization ◽  
The State ◽  
Estimation Methods ◽  
Estimation Accuracy ◽  
Training Process ◽  
Ordinal Ranking ◽  
Facial Age

Recently the state-of-the-art facial age estimation methods are almost originated from solving complicated mathematical optimization problems and thus consume huge quantities of time in the training process. To refrain from such algorithm complexity while maintaining a high estimation accuracy, we propose a multifeature extreme ordinal ranking machine (MFEORM) for facial age estimation. Experimental results clearly demonstrate that the proposed approach can sharply reduce the runtime (even up to nearly one hundred times faster) while achieving comparable or better estimation performances than the state-of-the-art approaches. The inner properties of MFEORM are further explored with more advantages.

scholarly journals Real-time Arabic scene text detection using fully convolutional neural networks

2021 ◽  
Vol 11 (2) ◽  
pp. 1634
Author(s):  
Rajae Moumen ◽  
Raddouane Chiheb ◽  
Rdouan Faizi
Keyword(s):  
Real Time ◽  
Data Augmentation ◽  
State Of The Art ◽  
Arabic Language ◽  
Text Detection ◽  
The State ◽  
Convolutional Network ◽  
Fully Convolutional Network ◽  
Scene Text Detection ◽  
Scene Text

The aim of this research is to propose a fully convolutional approach to address the problem of real-time scene text detection for Arabic language. Text detection is performed using a two-steps multi-scale approach. The first step uses light-weighted fully convolutional network: TextBlockDetector FCN, an adaptation of VGG-16 to eliminate non-textual elements, localize wide scale text and give text scale estimation. The second step determines narrow scale range of text using fully convolutional network for maximum performance. To evaluate the system, we confront the results of the framework to the results obtained with single VGG-16 fully deployed for text detection in one-shot; in addition to previous results in the state-of-the-art. For training and testing, we initiate a dataset of 575 images manually processed along with data augmentation to enrich training process. The system scores a precision of 0.651 vs 0.64 in the state-of-the-art and a FPS of 24.3 vs 31.7 for a VGG-16 fully deployed.

scholarly journals All-fiber tribo-ferroelectric synergistic electronics with high thermal-moisture stability and comfortability

2019 ◽  
Vol 10 (1) ◽  
Author(s):  
Weifeng Yang ◽  
Wei Gong ◽  
Chengyi Hou ◽  
Yun Su ◽  
Yinben Guo ◽  
...  
Keyword(s):  
Peak Power ◽  
State Of The Art ◽  
Real Life ◽  
Low Frequency ◽  
The State ◽  
Electrical Performance ◽  
Life Situation ◽  
Hierarchical Networks ◽  
Real Life Situation ◽  
Low Frequency Motion

AbstractDeveloping fabric-based electronics with good wearability is undoubtedly an urgent demand for wearable technologies. Although the state-of-the-art fabric-based wearable devices have shown unique advantages in the field of e-textiles, further efforts should be made before achieving “electronic clothing” due to the hard challenge of optimally unifying both promising electrical performance and comfortability in single device. Here, we report an all-fiber tribo-ferroelectric synergistic e-textile with outstanding thermal-moisture comfortability. Owing to a tribo-ferroelectric synergistic effect introduced by ferroelectric polymer nanofibers, the maximum peak power density of the e-textile reaches 5.2 W m−2 under low frequency motion, which is 7 times that of the state-of-the-art breathable triboelectric textiles. Electronic nanofiber materials form hierarchical networks in the e-textile hence lead to moisture wicking, which contributes to outstanding thermal-moisture comfortability of the e-textile. The all-fiber electronics is reliable in complicated real-life situation. Therefore, it is an idea prototypical example for electronic clothing.

Real-Time Communications in Wireless Sensor Networks

2010 ◽  
pp. 69-78
Author(s):  
Isabelle Augé-Blum ◽  
Fei Yang ◽  
Thomas Watteyne
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Real Time ◽  
State Of The Art ◽  
Communication Protocols ◽  
The State ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Future Trends ◽  
Wireless Links

This chapter presents the state-of-the-art of real-time communication in the challenging topic of Wireless Sensor Networks (WSNs). In real-time communication, the duration between the event which initiates the sending of a message, and the instant this message is received must be smaller than a known delay. Because topologies are extremely dynamic and not known priori, this type of constraint is very hard to meet in WSNs. In this chapter, the different communication protocols proposed in the literatures, together with their respective advantages and drawbacks, are discussed. We focus on MAC and routing because they are key layers in real-time communication. As most existing protocols are not suitable under realistic constraints where sensor nodes and wireless links are unreliable, we give, at the end of this chapter, some insights about future trends in designing real-time protocols. We hope to give the reader an overview of recent research works in this complex topic which we consider to be essential in critical applications.

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