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

2021 ◽  
Author(s):  
Anu Jagannath ◽  
Jithin Jagannath
Keyword(s):  
State Of The Art ◽  
Spectrum Management ◽  
Signal Classification ◽  
Electromagnetic Spectrum ◽  
Signal Recognition ◽  
Single Task ◽  
Communication Signals ◽  
Spectrum Monitoring ◽  
Task Learning ◽  
Wireless Signal

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.

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

2021 ◽  
Author(s):  
Anu Jagannath ◽  
Jithin Jagannath
Keyword(s):  
State Of The Art ◽  
Spectrum Management ◽  
Signal Classification ◽  
Electromagnetic Spectrum ◽  
Signal Recognition ◽  
Single Task ◽  
Communication Signals ◽  
Spectrum Monitoring ◽  
Task Learning ◽  
Wireless Signal

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.

scholarly journals A Survey on Deep Learning Techniques in Wireless Signal Recognition

2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
Xiaofan Li ◽  
Fangwei Dong ◽  
Sha Zhang ◽  
Weibin Guo
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Future Trends ◽  
Signal Recognition ◽  
Wireless Technology ◽  
Modulation Recognition ◽  
Spectrum Monitoring ◽  
Learning Techniques ◽  
Wireless Signal ◽  
New Challenges

Wireless signal recognition plays an important role in cognitive radio, which promises a broad prospect in spectrum monitoring and management with the coming applications for the 5G and Internet of Things networks. Therefore, a great deal of research and exploration on signal recognition has been done and a series of effective schemes has been developed. In this paper, a brief overview of signal recognition approaches is presented. More specifically, classical methods, emerging machine learning, and deep leaning schemes are extended from modulation recognition to wireless technology recognition with the continuous evolution of wireless communication system. In addition, the opening problems and new challenges in practice are discussed. Finally, a conclusion of existing methods and future trends on signal recognition is given.

scholarly journals A Multi-Branch U-Net for Steel Surface Defect Type and Severity Segmentation

Metals ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 870
Author(s):  
Robby Neven ◽  
Toon Goedemé
Keyword(s):  
Process Parameters ◽  
Visual Inspection ◽  
Steel Surface ◽  
Surface Defect ◽  
State Of The Art ◽  
Sheet Steel ◽  
Real Life ◽  
Single Task ◽  
Severity Estimation ◽  
Task Model

Automating sheet steel visual inspection can improve quality and reduce costs during its production. While many manufacturers still rely on manual or traditional inspection methods, deep learning-based approaches have proven their efficiency. In this paper, we go beyond the state-of-the-art in this domain by proposing a multi-task model that performs both pixel-based defect segmentation and severity estimation of the defects in one two-branch network. Additionally, we show how incorporation of the production process parameters improves the model’s performance. After manually constructing a real-life industrial dataset, we first implemented and trained two single-task models performing the defect segmentation and severity estimation tasks separately. Next, we compared this to a multi-task model that simultaneously performs the two tasks at hand. By combining the tasks into one model, both segmentation tasks improved by 2.5% and 3% mIoU, respectively. In the next step, we extended the multi-task model using sensor fusion with process parameters. We demonstrate that the incorporation of the process parameters resulted in a further mIoU increase of 6.8% and 2.9% for the defect segmentation and severity estimation tasks, respectively.

scholarly journals Multi-Task Learning Using Task Dependencies for Face Attributes Prediction

2019 ◽  
Vol 9 (12) ◽  
pp. 2535
Author(s):  
Di Fan ◽  
Hyunwoo Kim ◽  
Jummo Kim ◽  
Yunhui Liu ◽  
Qiang Huang
Keyword(s):  
Human Computer Interaction ◽  
Video Surveillance ◽  
State Of The Art ◽  
Learning Task ◽  
Face Verification ◽  
Task Learning ◽  
The World ◽  
In The Wild ◽  
And Gender ◽  
Learning Architectures

Face attributes prediction has an increasing amount of applications in human–computer interaction, face verification and video surveillance. Various studies show that dependencies exist in face attributes. Multi-task learning architecture can build a synergy among the correlated tasks by parameter sharing in the shared layers. However, the dependencies between the tasks have been ignored in the task-specific layers of most multi-task learning architectures. Thus, how to further boost the performance of individual tasks by using task dependencies among face attributes is quite challenging. In this paper, we propose a multi-task learning using task dependencies architecture for face attributes prediction and evaluate the performance with the tasks of smile and gender prediction. The designed attention modules in task-specific layers of our proposed architecture are used for learning task-dependent disentangled representations. The experimental results demonstrate the effectiveness of our proposed network by comparing with the traditional multi-task learning architecture and the state-of-the-art methods on Faces of the world (FotW) and Labeled faces in the wild-a (LFWA) datasets.

Research on Communication Reconnaissance Information Processing and Fusion

2014 ◽  
Vol 552 ◽  
pp. 359-362
Author(s):  
Hong Wei Quan ◽  
Dong Liang Peng
Keyword(s):  
Information Fusion ◽  
Electromagnetic Signal ◽  
Electromagnetic Spectrum ◽  
Simulation Environment ◽  
Characteristic Parameters ◽  
Communication Signals ◽  
Time Frequency ◽  
Blackboard Model ◽  
Processing Data ◽  
Signal Environment

In complex electromagnetic signal environment, the reconnaissance equipments in tactical communication system can uninterruptedly reconnoiter a variety of enemy’s communication signals as well as access a number of characteristic parameters of time, frequency and space domain by searching analysis, feature extraction, direction finding and comprehensive identification. After a series of signal processing, data mining and information fusion, we can get the characteristic parameters of the electromagnetic spectrum of the enemy’s reconnaissance equipments, which provide the basis for analysis and estimation of electromagnetic situation in battlefield. In this paper a multi-hierarchical blackboard model is proposed for multi-sources communication reconnaissance information mining and fusion and the effectiveness of the method is validated in simulation environment.

scholarly journals Parallel Crossed Chaotic Encryption for Hyperspectral Images

2018 ◽  
Vol 8 (7) ◽  
pp. 1183 ◽  
Author(s):  
Carlos Villaseñor ◽  
Eric Gutierrez-Frias ◽  
Nancy Arana-Daniel ◽  
Alma Alanis ◽  
Carlos Lopez-Franco
Keyword(s):  
Chaotic Systems ◽  
Ad Hoc ◽  
State Of The Art ◽  
Hyperspectral Images ◽  
Encryption Algorithm ◽  
Electromagnetic Spectrum ◽  
Chaotic Encryption ◽  
Raw Data ◽  
Essential Tool ◽  
Encryption Schemes

Hyperspectral images (HI) collect information from across the electromagnetic spectrum, and they are an essential tool for identifying materials, recognizing processes and finding objects. However, the information on an HI could be sensitive and must to be protected. Although there are many encryption schemes for images and raw data, there are not specific schemes for HI. In this paper, we introduce the idea of crossed chaotic systems and we present an ad hoc parallel crossed chaotic encryption algorithm for HI, in which we take advantage of the multidimensionality nature of the HI. Consequently, we obtain a faster encryption algorithm and with a higher entropy result than others state of the art chaotic schemes.

scholarly journals Learning to Incorporate Structure Knowledge for Image Inpainting

2020 ◽  
Author(s):  
Jie Yang ◽  
Zhiquan Qi ◽  
Yong Shi
Keyword(s):  
Structure Learning ◽  
State Of The Art ◽  
Image Inpainting ◽  
Image Completion ◽  
Image Structure ◽  
Learning Framework ◽  
Task Learning ◽  
Pyramid Structure ◽  
Benchmark Datasets ◽  
Structure Knowledge

This paper develops a multi-task learning framework that attempts to incorporate the image structure knowledge to assist image inpainting, which is not well explored in previous works. The primary idea is to train a shared generator to simultaneously complete the corrupted image and corresponding structures --- edge and gradient, thus implicitly encouraging the generator to exploit relevant structure knowledge while inpainting. In the meantime, we also introduce a structure embedding scheme to explicitly embed the learned structure features into the inpainting process, thus to provide possible preconditions for image completion. Specifically, a novel pyramid structure loss is proposed to supervise structure learning and embedding. Moreover, an attention mechanism is developed to further exploit the recurrent structures and patterns in the image to refine the generated structures and contents. Through multi-task learning, structure embedding besides with attention, our framework takes advantage of the structure knowledge and outperforms several state-of-the-art methods on benchmark datasets quantitatively and qualitatively.

scholarly journals A New Feature Extraction Algorithm Based on Entropy Cloud Characteristics of Communication Signals

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Jingchao Li ◽  
Jian Guo
Keyword(s):  
Feature Extraction ◽  
Cloud Model ◽  
Recognition Rate ◽  
Signal Recognition ◽  
Communication Signals ◽  
Low Snr ◽  
Feature Extraction Algorithm ◽  
Cloud Characteristics ◽  
Extraction Algorithm ◽  
New Feature

Identifying communication signals under low SNR environment has become more difficult due to the increasingly complex communication environment. Most relevant literatures revolve around signal recognition under stable SNR, but not applicable under time-varying SNR environment. To solve this problem, we propose a new feature extraction method based on entropy cloud characteristics of communication modulation signals. The proposed algorithm extracts the Shannon entropy and index entropy characteristics of the signals first and then effectively combines the entropy theory and cloud model theory together. Compared with traditional feature extraction methods, instability distribution characteristics of the signals’ entropy characteristics can be further extracted from cloud model’s digital characteristics under low SNR environment by the proposed algorithm, which improves the signals’ recognition effects significantly. The results from the numerical simulations show that entropy cloud feature extraction algorithm can achieve better signal recognition effects, and even when the SNR is −11 dB, the signal recognition rate can still reach 100%.

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