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

2021 ◽  
Author(s):  
Anu Jagannath ◽  
Jithin Jagannath
Keyword(s):  
Signal Classification ◽  
Learning Approach ◽  
Task Learning ◽  
Wireless Signal

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 Mask-guided Attention Deep Learning Model for COVID-19 Diagnosis based on an Integrated CT Scan Images Database

2022 ◽  
Author(s):  
Maede Maftouni ◽  
Bo Shen ◽  
Andrew Chung Chee Law ◽  
Niloofar Ayoobi Yazdi ◽  
Zhenyu Kong
Keyword(s):  
Deep Learning ◽  
Ct Scan ◽  
Imaging Modality ◽  
Learning Model ◽  
Classification Performance ◽  
Computer Assisted ◽  
Learning Approach ◽  
Learning Models ◽  
Task Learning ◽  
Data Efficiency

<p>The global extent of COVID-19 mutations and the consequent depletion of hospital resources highlighted the necessity of effective computer-assisted medical diagnosis. COVID-19 detection mediated by deep learning models can help diagnose this highly contagious disease and lower infectivity and mortality rates. Computed tomography (CT) is the preferred imaging modality for building automatic COVID-19 screening and diagnosis models. It is well-known that the training set size significantly impacts the performance and generalization of deep learning models. However, accessing a large dataset of CT scan images from an emerging disease like COVID-19 is challenging. Therefore, data efficiency becomes a significant factor in choosing a learning model. To this end, we present a multi-task learning approach, namely, a mask-guided attention (MGA) classifier, to improve the generalization and data efficiency of COVID-19 classification on lung CT scan images.</p><p>The novelty of this method is compensating for the scarcity of data by employing more supervision with lesion masks, increasing the sensitivity of the model to COVID-19 manifestations, and helping both generalization and classification performance. Our proposed model achieves better overall performance than the single-task baseline and state-of-the-art models, as measured by various popular metrics. In our experiment with different percentages of data from our curated dataset, the classification performance gain from this multi-task learning approach is more significant for the smaller training sizes. Furthermore, experimental results demonstrate that our method enhances the focus on the lesions, as witnessed by both</p><p>attention and attribution maps, resulting in a more interpretable model.</p>

scholarly journals A Multi-task Learning Approach for Image Captioning

2018 ◽  
Author(s):  
Wei Zhao ◽  
Benyou Wang ◽  
Jianbo Ye ◽  
Min Yang ◽  
Zhou Zhao ◽  
...  
Keyword(s):  
Object Classification ◽  
Classification Model ◽  
Learning Approach ◽  
Generation Model ◽  
Image Captioning ◽  
Generation Task ◽  
Task Learning ◽  
Image Representations ◽  
Multiple Domains ◽  
Image Descriptions

In this paper, we propose a Multi-task Learning Approach for Image Captioning (MLAIC ), motivated by the fact that humans have no difficulty performing such task because they possess capabilities of multiple domains. Specifically, MLAIC consists of three key components: (i) A multi-object classification model that learns rich category-aware image representations using a CNN image encoder; (ii) A syntax generation model that learns better syntax-aware LSTM based decoder; (iii) An image captioning model that generates image descriptions in text, sharing its CNN encoder and LSTM decoder with the object classification task and the syntax generation task, respectively. In particular, the image captioning model can benefit from the additional object categorization and syntax knowledge. To verify the effectiveness of our approach, we conduct extensive experiments on MS-COCO dataset. The experimental results demonstrate that our model achieves impressive results compared to other strong competitors.

A Multi-Task Learning Approach for Recommendation based on Knowledge Graph

2021 ◽  
Author(s):  
Cairong Yan ◽  
Shuai Liu ◽  
Yanting Zhang ◽  
Zijian Wang ◽  
Pengwei Wang
Keyword(s):  
Knowledge Graph ◽  
Learning Approach ◽  
Task Learning
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