检索结果-内蒙古大学图书馆

25th Opto-Electronics and Communications Conference (OECC)

作者： Liu, Xin Wei, Zixian Pepe, Alberto Wang, Zhaoming Fu, H. Y. Tsinghua Univ Tsinghua Berkeley Shenzhen Inst TBSI Shenzhen Peoples R China

We firstly establish an autoencoder-based optical wireless communication (OWC) system model superior over conventional 4/16 quadrature amplitude modulation (QAM) modulation format over long-distance atmospheric turbul... 详细信息

ISBN: (纸本)9781728154459

关键词： deep learning autoencoder optical wireless communication atmospheric turbulence OFDM

来源：评论

学校读者我要写书评

暂无评论

Variable Rate Deep Image Compression With Modulated autoencoder

引用

IEEE SIGNAL PROCESSING LETTERS 2020年第0期27卷 331-335页

作者： Yang, Fei Herranz, Luis van de Weijer, Joost Guitian, Jose A. Iglesias Lopez, Antonio M. Mozerov, Mikhail G. Univ Autonoma Barcelona Comp Vis Ctr E-08193 Barcelona Spain Northwestern Polytech Univ Key Lab Informat Fus Technol Xian 710072 Peoples R China Comp Vis Ctr Barcelona 08193 Spain Comp Sci Dept Barcelona 08193 Spain

Variable rate is a requirement for flexible and adaptable image and video compression. However, deep image compression methods (DIC) are optimized for a single fixed rate-distortion (R-D) tradeoff. While this can be addressed by training multiple models for different tradeoffs, the memory requirements increase proportionally to the number of models. Scaling the bottleneck representation of a shared autoencoder can provide variable rate compression with a single shared autoencoder. However, the R-D performance using this simple mechanism degrades in low bitrates, and also shrinks the effective range of bitrates. To address these limitations, we formulate the problem of variable R-D optimization for DIC, and propose modulated autoencoders (MAEs), where the representations of a shared autoencoder are adapted to the specific R-D tradeoff via a modulation network. Jointly training this modulated autoencoder and the modulation network provides an effective way to navigate the R-D operational curve. Our experiments show that the proposed method can achieve almost the same R-D performance of independent models with significantly fewer parameters.

关键词： Deep image compression variable bitrate autoencoder modulated autoencoder

来源：评论

学校读者我要写书评

暂无评论

Deep Learning Methods for Arabic autoencoder Speech Recognition System for Electro-Larynx Device

引用

ADVANCES IN HUMAN-COMPUTER INTERACTION 2023年第1期2023卷

作者： Ameen, Zinah J. Mohammed J. Kadhim, Abdulkareem Abdulrahman Al Nahrain Univ Coll Informat Engn Baghdad Iraq

Recent advances in speech recognition have achieved remarkable performance comparable with human transcribers' abilities. But this significant performance is not the same for all the spoken languages. The Arabic language is one of them. Arabic speech recognition is bounded to the lack of suitable datasets. Artificial intelligence algorithms have shown promising capabilities for Arabic speech recognition. Arabic is the official language of 22 countries, and it has been estimated that 400 million people speak the Arabic language worldwide. Speech disabilities have been one of the expanding problems in the last decades, even in kids. Some devices can be used to generate speech for those people. One of these devices is the Servox Digital Electro-Larynx (EL). In this research, we developed an autoencoder with a combination of long short-term memory (LSTM) and gated recurrent units (GRU) models to recognize recorded signals from Servox Digital EL Electro-Larynx. The proposed framework consisted of three steps: denoising, feature extraction, and Arabic speech recognition. The experimental results show 95.31% accuracy for Arabic speech recognition with the proposed model. In this research, we evaluated different combinations of LSTM and GRU for constructing the best autoencoder. A rigorous evaluation process indicates better performance with the use of GRU in both encoder and decoder structures. The proposed model achieved a 4.69% word error rate (WER). Experimental results confirm that the proposed model can be used for developing a real-time app to recognize common Arabic spoken words.

关键词： Speech recognition Speech autoencoder Deep learning Long short-term memory

来源：评论

学校读者我要写书评

暂无评论

Dual autoencoder Network with Separable Convolutional Layers for Denoising and Deblurring Images

引用

JOURNAL OF IMAGING 2022年第9期8卷 250-250页

作者： Solovyeva, Elena Abdullah, Ali St Petersburg Electrotech Univ LETI Dept Elect Engn Theory St Petersburg 197022 Russia

A dual autoencoder employing separable convolutional layers for image denoising and deblurring is represented. Combining two autoencoders is presented to gain higher accuracy and simultaneously reduce the complexity of neural network parameters by using separable convolutional layers. In the proposed structure of the dual autoencoder, the first autoencoder aims to denoise the image, while the second one aims to enhance the quality of the denoised image. The research includes Gaussian noise (Gaussian blur), Poisson noise, speckle noise, and random impulse noise. The advantages of the proposed neural network are the number reduction in the trainable parameters and the increase in the similarity between the denoised or deblurred image and the original one. The similarity is increased by decreasing the main square error and increasing the structural similarity index. The advantages of a dual autoencoder network with separable convolutional layers are demonstrated by a comparison of the proposed network with a convolutional autoencoder and dual convolutional autoencoder.

关键词： machine learning image processing computer vision image denoising autoencoder dual autoencoder convolutional neural network separable convolutional neural network deep learning non-linear model

来源：评论

学校读者我要写书评

暂无评论

Utilizing Half Convolutional autoencoder to Generate User and Item Vectors for Initialization in Matrix Factorization

引用

FUTURE INTERNET 2022年第1期14卷 20页

作者： Duong, Tan Nghia Doan, Nguyen Nam Do, Truong Giang Tran, Manh Hoang Nguyen, Duc Minh Dang, Quang Hieu Hanoi Univ Sci & Technol Sch Elect & Telecommun Hanoi 100000 Vietnam

Recommendation systems based on convolutional neural network (CNN) have attracted great attention due to their effectiveness in processing unstructured data such as images or audio. However, a huge amount of raw data produced by data crawling and digital transformation is structured, which makes it difficult to utilize the advantages of CNN. This paper introduces a novel autoencoder, named Half Convolutional autoencoder, which adopts convolutional layers to discover the high-order correlation between structured features in the form of Tag Genome, the side information associated with each movie in the MovieLens 20 M dataset, in order to generate a robust feature vector. Subsequently, these new movie representations, along with the introduction of users' characteristics generated via Tag Genome and their past transactions, are applied into well-known matrix factorization models to resolve the initialization problem and enhance the predicting results. This method not only outperforms traditional matrix factorization techniques by at least 5.35% in terms of accuracy but also stabilizes the training process and guarantees faster convergence.

关键词： autoencoder collaborative filtering convolutional neural network matrix factorization recommendation system

来源：评论

学校读者我要写书评

暂无评论

A multi-omics supervised autoencoder for pan-cancer clinical outcome endpoints prediction

引用

BMC MEDICAL INFORMATICS AND DECISION MAKING 2020年第Sup3期20卷 129-129页

作者： Tan, Kaiwen Huang, Weixian Hu, Jinlong Dong, Shoubin South China Univ Technol Sch Comp Sci & Engn Commun & Comp Network Lab Guangdong Wushan Rd Guangzhou 381 Guangdong Peoples R China

BackgroundWith the rapid development of sequencing technologies, collecting diverse types of cancer omics data become more cost-effective. Many computational methods attempted to represent and fuse multiple omics into a comprehensive view of cancer. However, different types of omics are related and heterogeneous. Most of the existing methods do not consider the difference between omics, so the biological knowledge of individual omics may not be fully excavated. And for a given task (e.g. predicting overall survival), these methods prefer to use sample similarity or domain knowledge to learn a more reasonable representation of omics, but it's not *** the purpose of learning more useful representation for individual omics and fusing them to improve the prediction ability, we proposed an autoencoder-based method named MOSAE (Multi-omics Supervised autoencoder). In our method, a specific autoencoder were designed for each omics according to their size of dimension to generate omics-specific representations. Then, a supervised autoencoder was constructed based on specific autoencoder by using labels to enforce each specific autoencoder to learn both omics-specific and task-specific representations. Finally, representations of different omics that generate from supervised autoencoders were fused in a traditional but powerful way, and the fused representation was used for subsequent predictive *** applied our method over TCGA Pan-Cancer dataset to predict four different clinical outcome endpoints (OS, PFI, DFI, and DSS). Compared with traditional and state-of-the-art methods, MOSAE achieved better predictive performance. We also tested the effects of each improvement, which all have a positive effect on predictive *** clinical outcome endpoints are very important for precision medicine and personalized medicine. And multi-omics fusion is an effective way to solve this problem. MOSAE is a powerful multi-omics fusion me

关键词： Multic-omics autoencoder Fusion Representation Pan-Cancer Endpoints

来源：评论

学校读者我要写书评

暂无评论

autoencoder Matrix Completion Based Indoor Localization 54

Autoencoder Matrix Completion Based Indoor Localization

引用

54th Asilomar Conference on Signals, Systems and Computers

作者： Ahriz, Iness Terre, Michel Njima, Wafa CNAM LAETITIA CEDRIC Lab Paris France

ISBN: (纸本)9780738131269

The widespread of mobile devices facilitated the of many new applications that provide services based on user's location. Several techniques have been presented to enable such a service even in indoor environments where Global Positioning System (GPS) has low localization accuracy. These methods use some environment measurements. The most popular are using Received Signal Strength (RSS) for user location estimation. Due to the propagation conditions in indoor environment, the RSS methods suffer from missing data problem where the RSS can be below the sensitivity of some receivers. To overcome this problem, we propose in this paper an RSS matrix completion strategy based on an autoencoder algorithm as a preprocessing step. This latter exhibits a good performance in data denoising problems and can be applied for matrix completion purpose. A neural network is then used on the recovered RSS matrix to estimate a user's position. The performance of the proposed scheme is evaluated in a simulated environment and compared with traditional method of matrix completion based on the gradient descend algorithm and its variant. The results show the outperformances of our system of between 1 and 3 meters gain on localization error.

关键词： autoencoder localization matrix completion

来源：评论

学校读者我要写书评

暂无评论

A Non-Intrusive Speech Intelligibility Estimation Method Based on Deep Learning Using autoencoder Features

引用

IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS 2020年第3期E103D卷 714-715页

作者： Kim, Yoonhee Yun, Deokgyu Lee, Hannah Choi, Seung Ho Seoul Natl Univ Sci & Technol Seoul South Korea

This paper presents a deep learning-based non-intrusive speech intelligibility estimation method using bottleneck features of autoencoder. The conventional standard non-intrusive speech intelligibility estimation method, P.563, lacks intelligibility estimation performance in various noise environments. We propose a more accurate speech intelligibility estimation method based on long-short term memory (LSTM) neural network whose input and output are an autoencoder bottleneck features and a short-time objective intelligence (STOI) score, respectively, where STOI is a standard tool for measuring intrusive speech intelligibility with reference speech signals. We showed that the proposed method has a superior performance by comparing with the conventional standard P.563 and mel-frequency cepstral coefficient (MFCC) feature-based intelligibility estimation methods for speech signals in various noise environments.

关键词： autoencoder bottleneck feature STOI deep learning long short-term memory (LSTM)

来源：评论

学校读者我要写书评

暂无评论

Fine-Grained Air Pollution Inference with Mobile Sensing Systems: A Weather-Related Deep autoencoder Model

引用

PROCEEDINGS OF THE ACM ON INTERACTIVE MOBILE WEARABLE AND UBIQUITOUS TECHNOLOGIES-IMWUT 2020年第2期4卷 1–21页

作者： Ma, Rui Liu, Ning Xu, Xiangxiang Wang, Yue Noh, Hae Young Zhang, Pei Zhang, Lin Tsinghua Univ Dept Elect Engn Beijing Peoples R China Carnegie Mellon Univ Dept Civil & Environm Engn Pittsburgh PA 15213 USA Carnegie Mellon Univ Dept Elect & Comp Engn Moffett Field CA 15213 USA Tsinghua Univ Tsinghua Berkeley Shenzhen Inst Shenzhen Peoples R China

Air pollution is a global health threat. Except static official air quality stations, mobile sensing systems are deployed for urban air pollution monitoring to achieve larger sensing coverage and greater sampling granularity. However, the data sparsity and irregularity also bring great challenges for pollution map recovery. To address these problems, we propose a deep autoencoder framework based inference algorithm. Under the framework, a partially observed pollution map formed by the irregular samples are input into the model, then an encoder and a decoder work together to recover the entire pollution map. Inside the decoder, we adopt a convolutional long short-term memory (ConvLSTM) model by revealing its physical interpretation with an atmospheric dispersion model, and further present a weather-related ConvLSTM to enable quasi real-time applications. To evaluate our algorithm, a half-year data collection was deployed with a real-world system on a coastal area including the Sino-Singapore Tianjin Eco-city in north China. With the resolution of 500 m x 500 m x 1 h, our offline method is proved to have high robustness against low sampling coverage and accidental sensor errors, obtaining 14.9% performance improvement over existing methods. Our quasi real-time model better captures the spatiotemporal dependencies in the pollution map with unevenly distributed samples than other real-time approaches, obtaining 4.2% error reduction.

关键词： autoencoder convlstm air pollution map mobile sensing networks

来源：评论

学校读者我要写书评

暂无评论

Local conformal autoencoder for standardized data coordinates

引用

PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA 2020年第49期117卷 30918-30927页

作者： Peterfreund, Erez Lindenbaum, Ofir Dietrich, Felix Bertalan, Tom Gavish, Matan Kevrekidis, Ioannis G. Coifman, Ronald R. Hebrew Univ Jerusalem Sch Comp Sci & Engn IL-9190401 Jerusalem Israel Yale Univ Program Appl Math New Haven CT 06520 USA Tech Univ Munich Dept Informat D-80333 Munich Germany Johns Hopkins Univ Dept Chem & Biomol Engn Baltimore MD 21218 USA

We propose a local conformal autoencoder (LOCA) for standardized data coordinates. LOCA is a deep learning-based method for obtaining standardized data coordinates from scientific measurements. Data observations are modeled as samples from an unknown, nonlinear deformation of an underlying Riemannian manifold, which is parametrized by a few normalized, latent variables. We assume a repeated measurement sampling strategy, common in scientific measurements, and present a method for learning an embedding in Rd that is isometric to the latent variables of the manifold. The coordinates recovered by our method are invariant to diffeomorphisms of the manifold, making it possible to match between different instrumental observations of the same phenomenon. Our embedding is obtained using LOCA, which is an algorithm that learns to rectify deformations by using a local z-scoring procedure, while preserving relevant geometric information. We demonstrate the isometric embedding properties of LOCA in various model settings and observe that it exhibits promising interpolation and extrapolation capabilities, superior to the current state of the art. Finally, we demonstrate LOCA's efficacy in single-site Wi-Fi localization data and for the reconstruction of three-dimensional curved surfaces from two-dimensional projections.

关键词： manifold learning autoencoder dimensionality reduction canonical coordinates

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：