Deep Learning and Reinforcement Learning for Autonomous Unmanned Aerial Systems: Roadmap for Theory to Deployment

Jagannath, Jithin; Jagannath, Anu; Furman, Sean; Gwin, Tyler

doi:10.1007/978-3-030-77939-9_2

Cited by 21 publications

(7 citation statements)

References 66 publications

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“…Keeping that in mind and for the benefit of reader who might be relatively new to deep learning, we provide brief tutorial regarding the core techniques used for RF fingerprinting. For a more comprehensive review we recommend the readers to [91].…”

Section: Deep Learning For Rf Fingerprintingmentioning

confidence: 99%

A comprehensive survey on radio frequency (RF) fingerprinting: Traditional approaches, deep learning, and open challenges

Jagannath

Kumar

2022

Computer Networks

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Section: Deep Learning For Rf Fingerprintingmentioning

confidence: 99%

A comprehensive survey on radio frequency (RF) fingerprinting: Traditional approaches, deep learning, and open challenges

Jagannath

Kumar

2022

Computer Networks

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“…For example, [19] analyzes the artistic representation of flash animation from the perspective of technology and culture and points out that flash animation is a new artistic means with a broad development prospect; [20] explores flash animation from the perspective of art and aesthetics and concludes that Flash animation has artistic characteristics such as motion, shape, hypothesis, interaction, synthesis, and fashion. [21] explores the application of flash animation technology in the creation of movies in terms of object modeling, scene construction, and picture rhythm; [4] studies the important role of Flash animation in Chinese online news communication; [5] preliminarily studies the visual language of flash animation and its application in web design, such as the visual representation of fonts The visual language of flash animation and its application in web design, such as the visual representation of fonts, the artistic characteristics of graphics and the formal laws of color design.…”

Section: Related Workmentioning

confidence: 99%

“…Generating Adversarial Networks. Generative adversarial networks (GANs) are a deep learning generative model, which belongs to unsupervised learning model, proposed in [21], and can be used to learn complex feature distribution, image style transfer, model generation and other tasks. As shown in Figure 7, the generative adversarial network mainly contains a generative model and a discriminative model, which play each other to complete the network training process.…”

Section: Scene Structure Model For Web Flash Animationmentioning

confidence: 99%

Deep Learning-Based 3D Shape Feature Extraction on Flash Animation Style

Zhu

Lee

2022

Wireless Communications and Mobile Computing

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Flash animation, as a kind of digital learning resource, is an important media for delivering information content, and more importantly, it is an important online learning resource with text, graphics, images, audio, video, interaction, dynamic effects, etc. Flash animation, with its powerful multimedia interaction and presentation capabilities, is widely used in distance education, high-quality course websites, Q&A platforms, etc. With the continuous development of deep learning, the 3D shape feature extraction method combined with deep learning has become a hot research topic. In this paper, we combine deep learning with traditional 3D shape feature extraction methods, so that we can not only break the bottleneck of nondeep learning methods but also improve the accuracy of 3D shape data classification and retrieval tasks, especially in the case of non-rigid 3D shapes. The scheme in this paper not only does not require a large number of training samples but also its feature extraction for flash animation is accurate. Experiments show that the success rate of accurate feature extraction of this paper’s scheme is higher than that of the state-of-the-art methods.

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“…Deep learning has made significant strides in the field of computer vision [15], [16], natural language processing [17], speech recognition [18], autonomous control [19], [20] etc. The comparatively slower pace of applied deep learning in wireless communication in contrast to other domains can be in part attributed to the lack of available large scale datasets for the diverse wireless communication problems.…”

Section: Related Workmentioning

confidence: 99%

Multi-task Learning Approach for Automatic Modulation and Wireless Signal Classification

Jagannath¹,

Jagannath²

2021

Preprint

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Wireless signal recognition is becoming increasingly more significant for spectrum monitoring, spectrum management, and secure communications. Consequently, it will become a key enabler with the emerging fifth-generation (5G) and beyond 5G communications, Internet of Things networks, among others. State-of-the-art studies in wireless signal recognition have only focused on a single task which in many cases is insufficient information for a system to act on. In this work, for the first time in the wireless communication domain, we exploit the potential of deep neural networks in conjunction with multi-task learning (MTL) framework to simultaneously learn modulation and signal classification tasks. The proposed MTL architecture benefits from the mutual relation between the two tasks in improving the classification accuracy as well as the learning efficiency with a lightweight neural network model. Additionally, we consider the problem of heterogeneous wireless signals such as radar and communication signals in the electromagnetic spectrum. Accordingly, we have shown how the proposed MTL model outperforms several state-of-the-art single-task learning classifiers while maintaining a lighter architecture and performing two signal characterization tasks simultaneously. Finally, we also release the only known open heterogeneous wireless signals dataset that comprises of radar and communication signals with multiple labels.

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Deep Learning and Reinforcement Learning for Autonomous Unmanned Aerial Systems: Roadmap for Theory to Deployment

Cited by 21 publications

References 66 publications

A comprehensive survey on radio frequency (RF) fingerprinting: Traditional approaches, deep learning, and open challenges

A comprehensive survey on radio frequency (RF) fingerprinting: Traditional approaches, deep learning, and open challenges

Deep Learning-Based 3D Shape Feature Extraction on Flash Animation Style

Multi-task Learning Approach for Automatic Modulation and Wireless Signal Classification

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