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IEEE International Conference on Communications (ICC)

作者： Luo, Xinlai Yin, Benshun Chen, Zhiyong Xia, Bin Wang, Jiangzhou Shanghai Jiao Tong Univ Sch Elect Informat & Elect Engn Shanghai Peoples R China Univ Kent Sch Engineerin Canterbury CT2 7NT Kent England

ISBN: (纸本)9781665426718

In this paper, we propose a semantic communication scheme for wireless relay channels based on autoencoder, named AESC, which encodes and decodes sentences from the semantic dimension. The autoencoder module is trained separately and acts as the channel encoder and decoder in the semantic communication system. It provides anti-noise performance for the system. Meanwhile, a novel semantic forward (SF) mode is designed for the relay node to forward the semantic information at the semantic level, especially for the scenarios that there is no common knowledge shared between the source and destination nodes. Numerical results show that the AESC achieves better stability performance than the traditional communication schemes, and the proposed SF mode provides a significant performance gain compared to the traditional forward protocols.

关键词： Semantic Communication autoencoder Relay Semantic Forward.

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Deep text clustering using stacked autoencoder

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MULTIMEDIA TOOLS AND APPLICATIONS 2022年第8期81卷 10861-10881页

作者： Hosseini, Soodeh Varzaneh, Zahra Asghari Shahid Bahonar Univ Kerman Fac Math & Comp Dept Comp Sci Kerman Iran Shahid Bahonar Univ Kerman Mahani Math Res Ctr Kerman Iran

Text data is a type of unstructured information, which is easily processed by a human, but it is hard for the computer to understand. Text mining techniques effectively discover meaningful information from text, which has received a great deal of attention in recent years. The aim of this study is to evaluate and analyze the comments and suggestions presented by Barez Iran Company. Barez is an unlabeled dataset. Extracting useful information from unlabeled large textual data by human to manually be very difficult and time consuming. Therefore, in this paper we analyze suggestions presented in Persian using BERTopic modeling for cluster analysis of the dataset. In BERTopic, each document belongs to a topic with a probability distribution. As a result, seven latent topics are found, covering a broad range of issues such as Installation, manufacture, correction, and device. Then we propose a novel deep text clustering based on hybrid of a stacked autoencoder and k-means clustering to organize text documents into meaningful groups for mining information from Barez data in an unsupervised method. Our data clustering has three main steps: 1) Text representation with a new pre-trained BERT model for language understanding called ParsBERT, 2) Text feature extraction based on based on a new architecture of stacked autoencoder to reduce the dimension of data to provide robust features for clustering, 3) Cluster the data by k-means clustering. We employ the Barez dataset to verify our work's effectiveness;Silhouette Score is used to evaluate the resulting clusters with the best value of 0.60 with 3 clusters grouping. Experimental evaluations demonstrate that the proposed algorithm clearly outperforms other clustering methods.

关键词： autoencoder Clustering BERT word embedding Topic modelling

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Hyperspectral anomaly detection based on adaptive weighting method combined with autoencoder and convolutional neural network

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INTERNATIONAL JOURNAL OF REMOTE SENSING 2022年第7期43卷 2638-2658页

作者： Hou, Minkai Wang, Tao Su, Yanzhao Cai, Yanping Cao, Jiping PLA Rocket Force Univ Engn Coll Combat Support 2 Tongxin RdHongqing St Xian Shaanxi Peoples R China

Deep learning has achieved excellent performance in the field of computer vision and gained attention in the field of hyperspectral anomaly detection (HAD). Convolutional neural networks (CNN) based on pixel pairing features have achieved a certain detection effect, which is limited by background complexity in practical applications. This study proposes an adaptive weighted HAD algorithm that combines an autoencoder (AE) and CNN. Labelled hyperspectral images (HSIs) are used to train a CNN as a binary classifier for similarity discrimination in the data preparation stage, where morphological attribute filtering (MAF) is performed on images in the spatial dimension of the HSIs to highlight anomaly. The spectral angle is used as a measure of reconstruction error to train the AE, and the reconstruction error obtained using the AE is used as an adaptive weight to calculate the anomaly score, which considerably eliminates the blurring of the boundary between the background and anomalies. Through comparisons with some state-of-the-art methods, the effectiveness of the proposed method in improving detection accuracy and increasing background and anomaly discrimination is verified on three real datasets.

关键词： autoencoder anomaly detection convolutional neural network hyperspectral morphological attribute filtering

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Convolutional autoencoder-Based Phase Shift Feedback Compression for Intelligent Reflecting Surface-Assisted Wireless Systems

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IEEE COMMUNICATIONS LETTERS 2022年第1期26卷 89-93页

作者： Yu, Xianhua Li, Dong Xu, Yongjun Liang, Ying-Chang Macau Univ Sci & Technol Fac Informat Technol Macau Peoples R China Chongqing Univ Posts & Telecommun Sch Commun & Informat Engn Chongqing 400065 Peoples R China Univ Elect Sci & Technol China Ctr Intelligent Networking & Commun CINC Chengdu 611731 Peoples R China

In recent years, intelligent reflecting surface (IRS) has emerged as a promising technology for 6G due to its potential/ability to significantly enhance energy- and spectrum-efficiency. To this end, it is crucial to adjust the phases of reflecting elements of the IRS, and most of the research works focus on how to optimize/quantize the phase for different optimization objectives. In particular, the quantized phase shift (QPS) is assumed to be available at the IRS, which, however, does not always hold and should be fed back to the IRS in practice. Unfortunately, the feedback channel is generally bandwidth-limited, which cannot support a huge amount of feedback overhead of the QPS particularly for a large number of reflecting elements and/or the quantization level of each reflecting element. In order to break this bottleneck, in this letter, we propose a convolutional autoencoder-based scheme, in which the QPS is compressed on the receiver side and reconstructed on the IRS side. In order to solve the problems of mismatched distribution and vanishing gradient, we remove the batch normalization (BN) layers and introduce a denoising module. By doing so, it is possible to achieve a high compression ratio with a reliable reconstruction accuracy in the bandwidth-limited feedback channel, and it is also possible to accommodate existing works assuming available QPS at the IRS. Simulation results confirm the high reconstruction accuracy of the feedback/compressed QPS through a feedback channel, and show that the proposed scheme can significantly outperform the existing compression algorithms.

关键词： Wireless communication Convolutional neural networks Computer architecture Quantization (signal) Noise reduction Image coding Decoding Intelligent reflecting surface quantized phase shift feedback compression autoencoder convolutional neural network

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Compressing Multiple Sensor Signals for Distributed Sensing by autoencoder Preprocessing 5

Compressing Multiple Sensor Signals for Distributed Sensing ...

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5th International Conference on Control and Robotics (ICCR)

作者： Wang, Junming Shu, Jing Li, Zheng Yu, Hongliu Tong, Raymond Kai-Yu Chinese Univ Hong Kong Dept Biomed Engn Shatin Hong Kong Peoples R China Chinese Univ Hong Kong Dept Surg Shatin Hong Kong Peoples R China Univ Shanghai Sci & Technol Inst Rehabil Engn & Technol Shanghai Peoples R China

ISBN: (纸本)9798350307627

This paper proposes an autoencoder-based approach to effectively extract sensor features by leveraging an autoencoder as a data preprocessing method. The autoencoder constrains the hidden units in a bottleneck structure, resulting in a compressed knowledge representation of sensor readings. In the latent space representation, the encoded data learns and describes the most prominent latent attributes of sensor readings. The algorithm is experimentally validated in a real-world setting, demonstrating its effectiveness in accurately extracting relevant features from sensor data. Nine flexible bending sensors are utilized for posture sensing of a bellow-shaped fluidic elastomer actuator. Compared to previous studies, the results demonstrate that valuable features can be extracted without employing a large dropout rate for overfitting prevention, while maintaining prediction accuracy and reducing the entire sensor signals to half. Additionally, the training time is reduced by 7.2%. By providing a reduced and featured input to the regression neural network, the proposed approach not only prevents overfitting but also alleviates the computational redundancy and complexity brought by an increasing number of sensors.

关键词： autoencoder flexible sensor distributed sensing soft robotics deep learning

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An autoencoder Based ASCII Art Generator 23

An Autoencoder Based ASCII Art Generator

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8th International Conference on Intelligent Information Technology (ICIIT)

作者： Kimura, Masaomi Iqbal, Mohammad Mukhlash, Imam Shibaura Inst Technol Koto Tokyo Japan Inst Teknol Sepuluh Nopember Surabaya East Java Indonesia

ISBN: (纸本)9781450399616

ASCII art is a way to represent an image with character shapes. It is common to carry ASCII art instead of displaying image files on Internet bulletin boards. Multibyte encodings contain various characters that are useful to shape an image. The essential idea required to get ASCII art is to approximate color distribution at the portion of a target image to a character shape. In this study, we make a machine learning model that learns the shapes of characters in a multibyte encoding to convert a partial image of a target image to a font image.

关键词： ASCII art synthesis autoencoder deep learning K-nearest neighborhood

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NGSO-To-GSO Satellite Interference Detection Based on autoencoder 34

NGSO-To-GSO Satellite Interference Detection Based on Autoen...

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IEEE 34th Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC)

作者： Saifaldawla, Almoatssimbillah Ortiz-Gomez, Flor G. Lagunas, Eva Daoud, Saed Chatzinotas, Symeon Univ Luxembourg Interdisciplinary Ctr Secur Reliabil & Trust SnT Luxembourg Luxembourg

ISBN: (纸本)9781665464833

Recently, non-geostationary orbit (NGSO) satellite communication constellations have regained popularity due to their ability to provide global coverage and lower-latency connectivity. However, the new wave of Low Earth Orbit (LEO) satellite constellations operate on the same spectral bands as legacy satellites in geosynchronous orbit (GSO), which concurrently access the electromagnetic spectrum. Even if international regulations are in place, such increased spectral congestion will result in interference events. Therefore, both regulator entities and GSO operators have a high interest in detecting illegal or unlicensed NGSO interference sources. In this work, we simulate a realistic downlink interference scenario by emulating an actual commercial NGSO orbit whose signal is eventually received in a GSO receiver that is pointed toward a specific GSO satellite. We design an autoencoder deep neural network and we evaluate its performance considering both time-series and frequency-domain series of the overall received samples. Extensive numerical results are presented, validating the interference detection accuracy and comparing both domains of inputs at the autoencoder.

关键词： NGSO GSO Spectrum Sharing Interference Detection Deep Learning autoencoder

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Combining autoencoder and Yolov6 Model for Classification and Disease Detection in Chickens 23

Combining Autoencoder and Yolov6 Model for Classification an...

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8th International Conference on Intelligent Information Technology (ICIIT)

作者： Khang Nguyen Hoang Huynh Nguyen Vu Nhu Hoang Tran Ngoc Luyl-Da Quach FPT Univ Cantho City Vietnam

ISBN: (纸本)9781450399616

Among the causes of reduced production is a chicken disease, which can negatively affect consumer health. With the advancement of computer vision technology and profound innovations in the field of research, it has become increasingly important to analyze disease images collected by sensors in chickens to analyze the possibility of infection conveniently and efficiently. Consequently, research proposes to identify lesions using the autoencoder and Yolov6 model to classify and detect diseases in chicken flocks. This model is suitable for different chicken breeds from many countries and regions. This method helps improve and enhance image recognition accuracy by incorporating the data enhancement method in the data preprocessing step. The results show that the value of val/mAP (average accuracy) obtained by the method proposed in this paper is 99.15%. Moreover, hit over 90% on the test dataset. This method can be applied to the early detection of disease-carrying chickens in the captive population, ensuring a quality food source for humans.

关键词： autoencoder Yolov6 data enhancement chickens disease

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A Method for Face Image Inpainting Based on autoencoder and Generative Adversarial Network 1

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10th Pacific Rim Symposium on Image and Video Technology

作者： Gao, Xinyi Nguyen, Minh Yan, Wei Qi Auckland Univ Technol Auckland 1010 New Zealand

ISBN: (数字)9783031264313

ISBN: (纸本)9783031264306;9783031264313

Face image inpainting has great value in the fields of computer vision and digital image processing. In this paper, we propose a face image inpainting method based on autoencoder and Generative Adversarial Network (GAN). The neural network for image inpainting consists of two parts, a generator and a discriminator. The autoencoder is used twice in the discriminator part, after the final inpainted image is generated by local discriminator and global discriminator. The final loss function is obtained by combining Generative Adversarial Loss and Mean Squared Error (MSE) Loss [20]. We improve and implement an image inpainting model with two evaluation metrics, namely, Peak Signal-tonoise Ratio (PSNR) and Structural similarity index measure (SSIM) [27], respectively. The proposed model for image inpainting is much more suitable for face image inpainting.

关键词： autoencoder Generative adversarial network Face image inpainting Convolutional neural network

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Orthogonal Features Based EEG Signals Denoising Using Fractional and Compressed One-Dimensional CNN autoencoder

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IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING 2022年 30卷 2474-2485页

作者： Nagar, Subham Kumar, Ahlad DA IICT Gandhinagar 382007 Gujarat India DA IICT Dept Informat & Commun Technol Gandhinagar 382007 Gujarat India

This paper presents a fractional one-dimensional convolutional neural network (CNN) autoencoder for denoising the Electroencephalogram (EEG) signals which often get contaminated with noise during the recording process, mostly due to muscle artifacts (MA), introduced by the movement of muscles. The existing EEG denoising methods make use of decomposition, thresholding and filtering techniques. In the proposed approach, EEG signals are first transformed to orthogonal domain using Tchebichef moments before feeding to the proposed architecture. A new hyper-parameter (alpha) is introduced which refers to the fractional order with respect to which gradients are calculated during back-propagation. It is observed that by tuning a, the quality of the restored signal improves significantly. Motivated by the high usage of portable low energy devices which make use of compressed deep learning architectures, the trainable parameters of the proposed architecture are compressed using randomized singular value decomposition (RSVD) algorithm. The experiments are performed on the standard EEG datasets, namely, Mendeley and Bonn. The study shows that the proposed fractional and compressed architecture performs better than existing state-of-the-art signal denoising methods.

关键词： EEG signal denoising convolutional neural networks autoencoder Tchebichef moments compression

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