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An autoencoder-Based I/Q Channel Interaction Enhancement Method for Automatic Modulation Recognition

IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2023年第7期72卷 9620-9625页

作者： Zhang, Fuxin Luo, Chunbo Xu, Jialang Luo, Yang Univ Elect Sci & Technol China Sch Informat & Commun Engn Chengdu 611731 Peoples R China

This article proposes an autoencoder-based method to enhance the information interaction between in-phase/quadrature (I/Q) channels of the input data for automatic modulation recognition (AMR). The proposed method utilizes an autoencoder built by fully-connected layers to correlate the features of I/Q data and obtain the interaction feature from the intermediate layer, which is concatenated together with the original I/Q data as model inputs. To accommodate the new data dimensions, a modification scheme for the existing representative deep learning based AMR (DL-AMR) models is presented. Experimental results show that our method can improve the recognition accuracy of the state-of-the-art baseline models, and has a smaller time overhead compared with complex-valued neural networks.

关键词： Automatic modulation recognition in-phase quadrature signal deep learning autoencoder

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An Integral Projection-Based Semantic autoencoder for Zero-Shot Learning

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IEEE ACCESS 2023年 11卷 85351-85360页

作者： Heyden, William Ullah, Habib Siddiqui, Muhammad Salman Al-Machot, Fadi Norwegian Univ Life Sci NMBU Fac Sci & Technol REALTEK N-1430 As Norway

Zero-shot Learning (ZSL) classification categorizes or predicts classes (labels) that are not included in the training set (unseen classes). Recent works proposed different semantic autoencoder (SAE) models where the encoder embeds a visual feature vector space into the semantic space and the decoder reconstructs the original visual feature space. The objective is to learn the embedding by leveraging a source data distribution, which can be applied effectively to a different but related target data distribution. Such embedding-based methods are prone to domain shift problems and are vulnerable to biases. We propose an integral projection-based semantic autoencoder (IP-SAE) where an encoder projects a visual feature space concatenated with the semantic space into a latent representation space. We force the decoder to reconstruct the visual-semantic data space. Due to this constraint, the visual-semantic projection function preserves the discriminatory data included inside the original visual feature space. The enriched projection forces a more precise reconstitution of the visual feature space invariant to the domain manifold. Consequently, the learned projection function is less domain-specific and alleviates the domain shift problem. Our proposed IP-SAE model consolidates a symmetric transformation function for embedding and projection, and thus, it provides transparency for interpreting generative applications in ZSL. Therefore, in addition to outperforming state-of-the-art methods considering four benchmark datasets, our analytical approach allows us to investigate distinct characteristics of generative-based methods in the unique context of zero-shot inference.

关键词： autoencoder generative modelling generative regularisation latent space linear transfor-mation semantic embedding visual projection zero-shot learning

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An end-to-end multi-scale network based on autoencoder for infrared and visible image fusion

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MULTIMEDIA TOOLS AND APPLICATIONS 2023年第13期82卷 20139-20156页

作者： Liu, Hongzhe Yan, Hua Sichuan Univ Coll Elect & Informat Engn Chengdu 610065 Sichuan Peoples R China

Infrared and visible image fusion aims to obtain a more informative fusion image by merging the infrared and visible images. However, the existing methods have some shortcomings, such as detail information loss, unclear boundaries, and not being end-to-end. In this paper, we propose an end-to-end network architecture for infrared and visible image fusion task. Our network contains three essential parts: encoders, residual fusion module, and decoder. First, we input infrared and visible images to two encoders to extract shallow features, respectively. Subsequently, the two sets of features are concatenated and fed to the residual fusion module to extract multi-scale features and fuse them adequately. Finally, the fused image is obtained by the decoder. We conduct objective and subjective experiments on two public datasets. The comparison results with the state-of-art methods prove that the fusion results of the proposed method have better objective metrics and contain more detail information and more explicit boundary.

关键词： autoencoder Image fusion Infrared image Visible image

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DCDA: CircRNA-Disease Association Prediction with Feed-Forward Neural Network and Deep autoencoder

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INTERDISCIPLINARY SCIENCES-COMPUTATIONAL LIFE SCIENCES 2024年第1期16卷 91-103页

作者： Turgut, Hacer Turanli, Beste Boz, Betuel Marmara Univ Dept Comp Engn TR-34854 Istanbul Turkiye Marmara Univ Dept Bioengn TR-34854 Istanbul Turkiye

Circular RNA is a single-stranded RNA with a closed-loop structure. In recent years, academic research has revealed that circular RNAs play critical roles in biological processes and are related to human diseases. The discovery of potential circRNAs as disease biomarkers and drug targets is crucial since it can help diagnose diseases in the early stages and be used to treat people. However, in conventional experimental methods, conducting experiments to detect associations between circular RNAs and diseases is time-consuming and costly. To overcome this problem, various computational methodologies are proposed to extract essential features for both circular RNAs and diseases and predict the associations. Studies showed that computational methods successfully predicted performance and made it possible to detect possible highly related circular RNAs for diseases. This study proposes a deep learning-based circRNA-disease association predictor methodology called DCDA, which uses multiple data sources to create circRNA and disease features and reveal hidden feature codings of a circular RNA-disease pair with a deep autoencoder, then predict the relation score of the pair by a deep neural network. Fivefold cross-validation results on the benchmark dataset showed that our model outperforms state-of-the-art prediction methods in the literature with the AUC score of 0.9794.

关键词： CircRNA CircRNA-disease association Deep learning autoencoder Neural network

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Deep learning based asymmetrical autoencoder for PAPR reduction of CP-OFDM systems

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ENGINEERING SCIENCE AND TECHNOLOGY-AN INTERNATIONAL JOURNAL-JESTECH 2024年 50卷

作者： Abdullah, Ezmin Dimyati, Kaharudin Muhamad, Wan Norsyafizan W. Shuhaimi, Nurain Izzati Mohamad, Roslina Hidayat, Nabil M. Univ Teknol MARA UiTM Coll Engn Sch Elect Engn WHiSNet Wireless High Speed Network Res Grp Shah Alam 40450 Selangor Malaysia Univ Malaya Fac Engn Ctr Adv Commun Res & Innovat ACRI Dept Elect Engn Kuala Lumpur 50603 Malaysia Univ Teknol Mara UiTM Sch Elect Engn Coll Engn Shah Alam 40450 Selangor Malaysia

Adoption of Orthogonal Frequency Division Multiplexing (OFDM) in 5th Generation New Radio (5G NR) as a multicarrier modulation technique allows high data rate transmission with lower complexity. However, problem such as peak to average power ratio (PAPR) has decreased the transmitter efficiency. Recently, several attempts have been made to reduce the high PAPR in OFDM utilizing deep learning (DL) based on autoencoder architecture. However, the proposed autoencoder using symmetrical autoencoder (SAE) is followed by high computational complexity at both transmitter and receiver as well as BER performance degradation. Since 5G NR focuses on massive 5G internet of things eco-system, a flexible carrier spacing is used to support diverse spectrum bands which is afterward named as Cyclic Prefix OFDM (CP-OFDM). In this study, we aimed to contribute to this growing area of research by exploring the potential of our proposed asymmetrical autoencoder (AAE) to reduce high PAPR in the CP-OFDM system. Four AAE models have been developed in this study and the performance of the models were evaluated based on comprehensive conditions such as data training at different corruption levels, cyclic prefix length, upsampling factors and loss function levels. The investigation of AAE in 5G CP-OFDM system has shown superior performance using a 5x1 AAE model that can reduce a substantial amount of PAPR, BER degradation and computational complexity compared to conventional SAE. This study lays the groundwork for future research into the asymmetrical approach of autoencoder especially in 5G and beyond networks.

关键词： autoencoder CP-OFDM Deep learning OFDM systems Peak -to -average power ratio

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High-Ratio Lossy Compression: Exploring the autoencoder to Compress Scientific Data

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IEEE TRANSACTIONS ON BIG DATA 2023年第1期9卷 22-36页

作者： Liu, Tong Wang, Jinzhen Liu, Qing Alibhai, Shakeel Lu, Tao He, Xubin Temple Univ Dept Comp & Informat Sci Philadelphia PA 19122 USA New Jersey Inst Technol Dept Elect & Comp Engn Newark NJ 07102 USA

Scientific simulations on high-performance computing (HPC) systems can generate large amounts of floating-point data per run. To mitigate the data storage bottleneck and lower the data volume, it is common for floating-point compressors to be employed. As compared to lossless compressors, lossy compressors, such as SZ and ZFP, can reduce data volume more aggressively while maintaining the usefulness of the data. However, a reduction ratio of more than two orders of magnitude is almost impossible without seriously distorting the data. In deep learning, the autoencoder technique has shown great potential for data compression, in particular with images. Whether the autoencoder can deliver similar performance on scientific data, however, is unknown. In this article, we for the first time conduct a comprehensive study on the use of autoencoders to compress real-world scientific data and illustrate several key findings on using autoencoders for scientific data reduction. We implement an autoencoder-based compression prototype to reduce floating-point data. Our study shows that the out-of-the-box implementation needs to be further tuned in order to achieve high compression ratios and satisfactory error bounds. Our evaluation results show that, for most of the test datasets, the tuned autoencoder outperforms SZ by up to 4X, and ZFP by up to 50X in compression ratios, respectively. Our practices and lessons learned in this work can direct future optimizations for using autoencoders to compress scientific data.

关键词： Compressors Image coding Prototypes Big Data Decoding Data models Tuning Lossy data compression autoencoder machine learning scientific data

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Mobile applications identification using autoencoder based electromagnetic side channel analysis

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JOURNAL OF INFORMATION SECURITY AND APPLICATIONS 2023年 75卷

作者： Zhang, Jinghui Liang, Boxi Zhang, Hancheng Zhang, Wei Ling, Zhen Yang, Ming Southeast Univ Sch Comp Sci & Engn Nanjing 211189 Peoples R China Southeast Univ Sch Transportat Nanjing 211189 Peoples R China China United Network Commun Grp Co Ltd Digital Dept Beijing Peoples R China

Various applications are deployed on mobile smart devices in almost every situations of our life, while in some of these situations sensitive applications are strictly prohibited, such as cameras in cinemas and browsers in examination halls. Real-time recognition of applications running on mobile smart devices is of great significance in these cases. However, most of the existing technologies have the limitation that they require system permissions to obtain the running application list which is banned by mainstream mobile operating systems. Noting that the launch of a certain application will emit a unique pattern of magnetic field, we introduce magnetic field side channel analysis to recognize running applications. However, magnetic field side channel analysis is challenging since it is hard to extract features from magnetic field data without domain experts. Besides, real-time applications identification demands accurate detection of applications launching. To overcome these challenges, we extract robust depth features using autoencoder and implement online application recognition by introducing finite-state machine to identify the application launch window from raw data. The proposed method is evaluated by recognizing 1000 different applications in real environment. The experiment results show that the proposed method is feasible and effective in real-time application identification.

关键词： Mobile smart devices Applications identification Magnetic field Side channel analysis autoencoder Finite-state machine

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Feature extraction of linear separability using robust autoencoder with distance metric

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2023年第5期44卷 7589-7598页

作者： Wei, Pingping Zhang, Xin Yunnan Technol & Business Univ Sch Intelligent Sci & Engn Kunming Yunnan Peoples R China

This paper proposes a robust autoencoder withWasserstein distance metric to extract the linear separability features from the input data. To minimize the difference between the reconstructed feature space and the original feature space, using Wasserstein distance realizes a homeomorphic transformation of the original feature space, i.e., the so-called the reconstruction of feature space. The autoencoder is used for features extraction of linear separability in the reconstructed feature space. Experiment results on real datasets show that the proposed method reaches up 0.9777 and 0.7112 on the low-dimensional and high-dimensional datasets in extracted accuracies, respectively, and also outperforms competitors. Results also confirm that compared with feature metric-based methods and deep network architectures-based method, the linear separabilities of those features extracted by distance metric-based methods win over them. More importantly, the linear separabilities of those features obtained by evaluating distance similarity of the data are better than those obtained by evaluating feature importance of data. We also demonstrate that the data distribution in the feature space reconstructed by a homeomorphic transformation can be closer to the original data distribution.

关键词： autoencoder distance measure feature extraction linear separability

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Towards an Interpretable autoencoder: A Decision-Tree-Based autoencoder and its Application in Anomaly Detection

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IEEE TRANSACTIONS ON DEPENDABLE AND SECURE COMPUTING 2023年第2期20卷 1048-1059页

作者： Aguilar, Diana Laura Medina-Perez, Miguel Angel Loyola-Gonzalez, Octavio Choo, Kim-Kwang Raymond Bucheli-Susarrey, Edoardo Tecnol Monterrey Atizapan 52926 Estado De Mexic Mexico Altair Management Consultants Madrid 28006 Spain Univ Texas San Antonio Dept Informat Syst & Cyber Secur San Antonio TX 78249 USA

The importance of understanding and explaining the associated classification results in the utilization of artificial intelligence (AI) in many different practical applications (e.g., cyber security and forensics) has contributed to the trend of moving away from black-box / opaque AI towards explainable AI (XAI). In this article, we propose the first interpretable autoencoder based on decision trees, which is designed to handle categorical data without the need to transform the data representation. Furthermore, our proposed interpretable autoencoder provides a natural explanation for experts in the application area. The experimental findings show that our proposed interpretable autoencoder is among the top-ranked anomaly detection algorithms, along with one-class Support Vector Machine (SVM) and Gaussian Mixture. More specifically, our proposal is on average 2% below the best Area Under the Curve (AUC) result and 3% over the other Average Precision scores, in comparison to One-class SVM, Isolation Forest, Local Outlier Factor, Elliptic Envelope, Gaussian Mixture Model, and eForest.

关键词： Interpretable artificial intelligence autoencoder decision tree anomaly detection explainable artificial intelligence (XAI)

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The Hybrid Trip Destination Prediction Model of Vehicles Based on autoencoder and High-Order Interaction Features

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2023年第8期24卷 8443-8451页

作者： He, Yuchu Jia, Zhijuan Hu, Mingsheng Zhang, Geng Dong, Hanjie Zhengzhou Normal Univ Sch Informat Sci & Technol Zhengzhou 450044 Peoples R China Henan Univ Sch Comp & Informat Engn Kaifeng 475001 Peoples R China Southwest Univ Coll Comp & Informat Sci Chongqing 400715 Peoples R China Zhengzhou Univ Light Ind Coll Comp & Commun Engn Zhengzhou 450002 Peoples R China

Through the intelligent vehicles trip data collection, processing and analysis, so as to predict the vehicles trip destination, this technology can improve the user's driving experience and the city traffic conditions. The vehicles dispatching system can also judge the real-time road conditions according to the prediction results and plan a more reasonable and efficient driving route, which is of great significance to urban traffic planning and urban construction planning. However, the amount of information in the vehicles data is less, which cannot meet the training needs of some artificial intelligence models. In addition, due to communication technology and other issues, there is a certain degree of deviation between the GPS data of the vehicles and the real data. The previous vehicles destination prediction model did not well eliminate the negative impact of this deviation. In addition, the previous model linearly adds the characteristics of each vehicles's trip data, which cannot reflect the complex rules of the vehicles's trip destination. Therefore, to address the above-mentioned drawbacks, we propose a novel vehicle trip destination prediction method named Hybrid Trip Destination Prediction Model of Vehicle Based on autoencoder and High-Order Interaction Features (HAHIF). The HAHIF model extracts robust hidden features using the autoencoder model and considers the second-order association between them using a factorization machine to improve its superiority and effectiveness. Compared with mainstream benchmarks, the HAHIF model has an MSE value of 0.096, an RMSE value of 0.427, and a MAE value of 0.203 on the public dataset, both of which are the first place, which verifies that the HAHIF model has good predictive ability.

关键词： Trip destination prediction vehicles trajectory mining neural networks autoencoder

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