scholarly journals Deep Learning-Based Spectrum Prediction Collision Avoidance for Hybrid Wireless Environments

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 45818-45830 ◽  
Author(s):  
Ruben Mennes ◽  
Maxim Claeys ◽  
Felipe A. P. De Figueiredo ◽  
Irfan Jabandzic ◽  
Ingrid Moerman ◽  
...  
Keyword(s):  
Deep Learning ◽  
Collision Avoidance ◽  
Spectrum Prediction ◽  
Wireless Environments

Autonomous Ship Navigation Under Deep Learning and the Challenges in COLREGs

2018 ◽  
Author(s):  
Lokukaluge P. Perera
Keyword(s):  
Decision Making ◽  
Deep Learning ◽  
Collision Avoidance ◽  
Situation Awareness ◽  
Type System ◽  
Performance Standards ◽  
Transportation Systems ◽  
Agent Based ◽  
Distributed Intelligence ◽  
System Intelligence

A general framework to support the navigation side of autonomous ships is discussed in this study. That consists of various maritime technologies to achieve the required level of ocean autonomy. Decision-making processes in autonomous vessels will play an important role under such ocean autonomy, therefore the same technologies should consist of adequate system intelligence. Each onboard application in autonomous vessels may require localized decision-making modules, therefore that will introduce a distributed intelligence type strategy. Hence, future ships will be agent-based systems with distributed intelligence throughout vessels. The main core of this agent should consist of deep learning type technology that has presented promising results in other transportation systems, i.e. self-driving cars. Deep learning can capture helmsman behavior, therefore that type system intelligence can be used to navigate autonomous vessels. Furthermore, an additional decision support layer should also be developed to facilitate deep learning type technology including situation awareness and collision avoidance. Ship collision avoidance is regulated by the Convention on the International Regulations for Preventing Collisions at Sea, 1972 (COLREGs) under open sea areas. Hence, a general overview of the COLREGs and its implementation challenges, i.e. regulatory failures and violations, under autonomous ships are also discussed with the possible solutions as the main contribution of this study. Furthermore, additional considerations, i.e. performance standards with the applicable limits of liability, terms, expectations and conditions, towards evaluating ship behavior as an agent-based system on collision avoidance situations are also illustrated in this study.

Machine Learning in 5G Multimedia Communications

2021 ◽  
pp. 204-225
Author(s):  
Dragorad A. Milovanovic ◽  
Zoran S. Bojkovic ◽  
Dragan D. Kukolj
Keyword(s):  
Machine Learning ◽  
Wireless Networks ◽  
Deep Learning ◽  
Complex Networks ◽  
Future Research ◽  
Fifth Generation ◽  
Data Formats ◽  
Research Challenges ◽  
Wireless Environments ◽  
Special Case

Machine learning (ML) has evolved to the point that this technique enhances communications and enables fifth-generation (5G) wireless networks. ML is great to get insights about complex networks that use large amounts of data, and for predictive and proactive adaptation to dynamic wireless environments. ML has become a crucial technology for mobile broadband communication. Special case goes to deep learning (DL) in immersive media. Through this chapter, the goal is to present open research challenges and applications of ML. An exploration of the potential of ML-based solution approaches in the context of 5G primary eMBB, mMTC, and uHSLLC services is presented, evaluating at the same time open issues for future research, including standardization activities of algorithms and data formats.

scholarly journals pDeep3: Towards More Accurate Spectrum Prediction with Fast Few-Shot Learning

2020 ◽  
Author(s):  
Ching Tarn ◽  
Wen-Feng Zeng ◽  
Zhengcong Fei ◽  
Si-Min He
Keyword(s):  
Deep Learning ◽  
Data Quality ◽  
Prediction Accuracy ◽  
Source Code ◽  
Experimental Results ◽  
Collision Induced Dissociation ◽  
Learning Method ◽  
Energy Collision ◽  
Spectrum Prediction ◽  
The Difference

AbstractSpectrum prediction using deep learning has attracted a lot of attention in recent years. Although existing deep learning methods have dramatically increased the prediction accuracy, there is still considerable space for improvement, which is presently limited by the difference of fragmentation types or instrument settings. In this work, we use the few-shot learning method to fit the data online to make up for the shortcoming. The method is evaluated using ten datasets, where the instruments includes Velos, QE, Lumos, and Sciex, with collision energies being differently set. Experimental results show that few-shot learning can achieve higher prediction accuracy with almost negligible computing resources. For example, on the dataset from a untrained instrument Sciex-6600, within about 10 seconds, the prediction accuracy is increased from 69.7% to 86.4%; on the CID (collision-induced dissociation) dataset, the prediction accuracy of the model trained by HCD (higher energy collision dissociation) spectra is increased from 48.0% to 83.9%. It is also shown that, the method is not critical to data quality and is sufficiently efficient to fill the accuracy gap. The source code of pDeep3 is available at http://pfind.ict.ac.cn/software/pdeep3.

Terahertz Metasurface Spectrum Prediction Based On Deep Learning

2021 ◽  
Author(s):  
Jun Zhou ◽  
Zheng Zhu ◽  
Jiajia Qian ◽  
Zhenzhen Ge ◽  
Shuting Wu
Keyword(s):  
Deep Learning ◽  
Spectrum Prediction

Machine Learning in 5G Multimedia Communications

2020 ◽  
pp. 361-382
Author(s):  
Dragorad A. Milovanovic ◽  
Zoran S. Bojkovic ◽  
Dragan D. Kukolj
Keyword(s):  
Machine Learning ◽  
Wireless Networks ◽  
Deep Learning ◽  
Complex Networks ◽  
Future Research ◽  
Fifth Generation ◽  
Data Formats ◽  
Research Challenges ◽  
Wireless Environments ◽  
Special Case

Machine learning (ML) has evolved to the point that this technique enhances communications and enables fifth-generation (5G) wireless networks. ML is great to get insights about complex networks that use large amounts of data, and for predictive and proactive adaptation to dynamic wireless environments. ML has become a crucial technology for mobile broadband communication. Special case goes to deep learning (DL) in immersive media. Through this chapter, the goal is to present open research challenges and applications of ML. An exploration of the potential of ML-based solution approaches in the context of 5G primary eMBB, mMTC, and uHSLLC services is presented, evaluating at the same time open issues for future research, including standardization activities of algorithms and data formats.

Deep Learning for Spectrum Prediction from Spatial-Temporal-Spectral Data

2020 ◽  
pp. 1-1
Author(s):  
Xi Li ◽  
Zhicheng Liu ◽  
Guojun Chen ◽  
Yinfei Xu ◽  
Tiecheng Song
Keyword(s):  
Deep Learning ◽  
Spectral Data ◽  
Spectrum Prediction

scholarly journals Collision Avoidance Using Deep Learning-Based Monocular Vision

2021 ◽  
Vol 2 (5) ◽  
Author(s):  
Róbert-Adrian Rill ◽  
Kinga Bettina Faragó
Keyword(s):  
Deep Learning ◽  
Collision Avoidance ◽  
Depth Estimation ◽  
Autonomous Driving ◽  
Monocular Vision ◽  
Estimation Methods ◽  
Simple Method ◽  
Crash Avoidance ◽  
Monocular Depth ◽  
Similar Accuracy

AbstractAutonomous driving technologies, including monocular vision-based approaches, are in the forefront of industrial and research communities, since they are expected to have a significant impact on economy and society. However, they have limitations in terms of crash avoidance because of the rarity of labeled data for collisions in everyday traffic, as well as due to the complexity of driving situations. In this work, we propose a simple method based solely on monocular vision to overcome the data scarcity problem and to promote forward collision avoidance systems. We exploit state-of-the-art deep learning-based optical flow and monocular depth estimation methods, as well as object detection to estimate the speed of the ego-vehicle and to identify the lead vehicle, respectively. The proposed method utilizes car stop situations as collision surrogates to obtain data for time to collision estimation. We evaluate this approach on our own driving videos, collected using a spherical camera and smart glasses. Our results indicate that similar accuracy can be achieved on both video sources: the external road view from the car’s, and the ego-centric view from the driver’s perspective. Additionally, we set forth the possibility of using spherical cameras as opposed to traditional cameras for vision-based automotive sensing.

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