检索结果-内蒙古大学图书馆

Design and development of an indoor navigation system using denoising autoencoder based convolutional neural network for visually impaired people

引用

MULTIMEDIA TOOLS AND APPLICATIONS 2022年第3期81卷 3483-3514页

作者： Akilandeswari, J. Jothi, G. Naveenkumar, A. Sabeenian, R. S. Iyyanar, P. Paramasivam, M. E. Sona Coll Technol Dept IT Salem 636005 Tamil Nadu India Sona Coll Arts & Sci Dept BCA Salem 636005 Tamil Nadu India Sona Coll Technol Dept ECE Salem 636005 Tamil Nadu India

A challenging area of research is the development of a navigation system for visually impaired people in an indoor environment such as a railway station, commercial complex, educational institution, and airport. Identifying the current location of the users can be a difficult task for those with visual impairments. The entire selection of the navigation path depends upon the current location of the user. This work presents a detailed analysis of the recent user positioning techniques and methodologies on the indoor navigation system based on the parameters, such as techniques, cost, the feasibility of implementation, and limitations. This paper presents a denoising auto encoder based on the convolutional neural network (DAECNN) to identify the present location of the users. The proposed approach uses the de-noising autoencoder to reconstruct the noisy image and the convolution neural network (CNN) to classify the users' current position. The proposed method is compared with the existing deep learning approaches such as deep autoencoder, sparse autoencoder, CNN, multilayer perceptron, radial basis function neural network, and the performances are analyzed. The experimental findings indicate that the DAECNN methodology works better than the existing classification approaches.

关键词： denoising autoencoder Convolutional neural network Indoor navigation Classification User positioning Deep learning

来源：评论

学校读者我要写书评

暂无评论

Environment-dependent denoising autoencoder for distant-talking speech recognition

引用

EURASIP JOURNAL ON ADVANCES IN SIGNAL PROCESSING 2015年第1期2015卷 1-11页

作者： Ueda, Yuma Wang, Longbiao Kai, Atsuhiko Ren, Bo Shizuoka Univ Grad Sch Engn Naka Ku Hamamatsu Shizuoka 4328561 Japan Nagaoka Univ Technol 1603-1 Kamitomioka Nagaoka Niigata 9402188 Japan

In this paper, we propose an environment-dependent denoising autoencoder (DAE) and automatic environment identification based on a deep neural network (DNN) with blind reverberation estimation for robust distant-talking speech recognition. Recently, DAEs have been shown to be effective in many noise reduction and reverberation suppression applications because higher-level representations and increased flexibility of the feature mapping function can be learned. However, a DAE is not adequate in mismatched training and test environments. In a conventional DAE, parameters are trained using pairs of reverberant speech and clean speech under various acoustic conditions (that is, an environment-independent DAE). To address the above problem, we propose two environment-dependent DAEs to reduce the influence of mismatches between training and test environments. In the first approach, we train various DAEs using speech from different acoustic environments, and the DAE for the condition that best matches the test condition is automatically selected (that is, a two-step environment-dependent DAE). To improve environment identification performance, we propose a DNN that uses both reverberant speech and estimated reverberation. In the second approach, we add estimated reverberation features to the input of the DAE (that is, a one-step environment-dependent DAE or a reverberation-aware DAE). The proposed method is evaluated using speech in simulated and real reverberant environments. Experimental results show that the environment-dependent DAE outperforms the environment-independent one in both simulated and real reverberant environments. For two-step environment-dependent DAE, the performance of environment identification based on the proposed DNN approach is also better than that of the conventional DNN approach, in which only reverberant speech is used and reverberation is not blindly estimated. And, the one-step environment-dependent DAE significantly outperforms the two-step

关键词： Speech recognition Dereverberation denoising autoencoder Environment identification Distant-talking speech

来源：评论

学校读者我要写书评

暂无评论

Reconstruction model of three-dimensional ocean sound speed field based on Tucker-denoising autoencoder

引用

APPLIED ACOUSTICS 2024年 223卷

作者： Zhao, Jiabao Wang, Maofa Hu, Ning Zhu, Zhenjing Li, Houwei Wang, Yangzhen Liu, Darui Hangzhou Dianzi Univ Coll Elect & Informat Hangzhou Peoples R China Hangzhou Dianzi Univ Coll Mech Engn Hangzhou Peoples R China Hangzhou Dianzi Univ Marine Technol & Equipment Res Ctr Hangzhou Peoples R China

The reconstruction of the three-dimensional (3D) ocean sound speed field (SSF) is essential for the application of underwater sound equipment. Although traditional observation platforms equipped with sensor equipment can accurately measure the ocean sound speed, they often provide only point or line profiles, lacking comprehensive data for the entire sea area. Currently, there is limited research on reconstructing 3D ocean SSF, and existing models have yielded unsatisfactory results. This paper considers the main factors that influence ocean SSF and proposes a model that indirectly reconstructs it by utilizing temperature and salinity field data. The model is based on the Tucker decomposition algorithm and denoising autoencoder network. It takes as input temperature and salt depth data observed by Conductivity-Temperature-Depth (CTD) sensors during sea tests and outputs 3D ocean SSF data. The study compares the Tuckers-denoising autoencoder based approach with traditional methods such as kriging interpolation, Empirical Orthogonal Function (EOF), and dictionary learning techniques. The results indicate that the Tuckers-denoising autoencoder based approach outperforms traditional models by more than 38% in sparse regions of measured data, providing better approximations to actual values. This statement validates the accuracy and effectiveness of our proposed model, providing a practical and efficient approach for reconstructing 3D SSF in the ocean.

关键词： Three-dimensional ocean sound speed field Tucker algorithm denoising autoencoder Reconstruct

来源：评论

学校读者我要写书评

暂无评论

Detection of Freezing of Gait Using Unsupervised Convolutional denoising autoencoder

引用

IEEE ACCESS 2021年 9卷 115700-115709页

作者： Noor, Mohd Halim Mohd Nazir, Amril Ab Wahab, Mohd Nadhir Ling, Jodene Ooi Yen Univ Sains Malaysia Sch Comp Sci George Town 11800 Malaysia Zayed Univ Coll Technol Innovat Dept Informat Syst Abu Dhabi U Arab Emirates

At the advanced stage of Parkinson's disease, patients may suffer from 'freezing of gait' episodes: a debilitating condition wherein a patient's "feet feel as though they are glued to the floor." The objective, continuous monitoring of the gait of Parkinson's disease patients with wearable devices has led to the development of many freezing of gait detection models involving the automatic cueing of a rhythmic auditory stimulus to shorten or prevent episodes. The use of thresholding and manually extracted features or feature engineering returned promising results. However, these approaches are subjective, time-consuming, and prone to error. Furthermore, their performance varied when faced with the different walking styles of Parkinson's disease patients. Inspired by state-of-art deep learning techniques, this research aims to improve the detection model by proposing a feature learning deep denoising autoencoder to learn the salient characteristics of Parkinsonian gait data that is applicable to different walking styles for the elimination of manually handcrafted features. Even with the elimination of manually handcrafted features, a reduction in half of the data window sizes to 2s, and a significant dimensionality reduction of learned features, the detection model still managed to achieve 90.94% sensitivity and 67.04% specificity, which is comparable to the original Daphnet dataset research.

关键词： Feature extraction Legged locomotion Support vector machines Diseases Sensitivity Data models Sensor phenomena and characterization Parkinson's disease freezing of gait denoising autoencoder unsupervised learning

来源：评论

学校读者我要写书评

暂无评论

DEVDAN: Deep evolving denoising autoencoder

引用

NEUROCOMPUTING 2020年 390卷 297-314页

作者： Ashfahani, Andri Pratama, Mahardhika Lughofer, Edwin Ong, Yew Soon Nanyang Technol Univ Sch Comp Sci & Engn Singapore Singapore Johannes Kepler Univ Linz Linz Austria

The denoising autoencoder (DAE) enhances the flexibility of data stream method in exploiting unlabeled samples. Nonetheless, the feasibility of DAE for data stream analytic deserves in-depth study because it characterizes a fixed network capacity which cannot adapt to rapidly changing environments. Deep evolving denoising autoencoder (DEVDAN), is proposed in this paper. It features an open structure in the generative phase and the discriminative phase where the hidden units can be automatically added and discarded on the fly. The generative phase refines the predictive performance of discriminative model exploiting unlabeled data. Furthermore, DEVDAN is free of the problem-specific threshold and works fully in the single-pass learning fashion. We show that DEVDAN can find competitive network architecture compared with state-of-the-art methods on the classification task using ten prominent datasets simulated under the prequential test-then-train protocol. (C) 2019 Elsevier B.V. All rights reserved.

关键词： denoising autoencoder Data streams Incremental learning

来源：评论

学校读者我要写书评

暂无评论

V-DAFT: visual technique for texture image defect recognition with denoising autoencoder and fourier transform

引用

SIGNAL IMAGE AND VIDEO PROCESSING 2024年第10期18卷 7405-7418页

作者： Si, Jongwook Kim, Sungyoung Kumoh Natl Inst Technol Dept Computer AI Convergence Engn Daehak Ro 61 Gumi 39177 Gyeongbuk South Korea Kumoh Natl Inst Technol Dept Comp Engn Daehak Ro 61 Gumi 39177 Gyeongbuk South Korea

Texture is the surface qualities and visual attributes of an object, determined by the arrangement, size, shape, density, and proportion of its fundamental components. In the manufacturing industry, products typically have uniform textures, allowing for automated visual inspections of the product surface to recognize defects. During this process, texture defect recognition techniques can be employed. In this paper, we propose a method that combines a convolutional autoencoder architecture with Fourier transform analysis. We employ a normal reconstructed template as defined in this study. Despite its simple structure and rapid training and inference capabilities, it offers recognition performance comparable to state-of-the-art methods. Fourier transform is a powerful tool for analyzing the frequency domain of images and signals, which is essential for effective defect recognition as texture defects often exhibit characteristic changes in specific frequency ranges. The experiment evaluates the recognition performance using the AUC metric, with the proposed method showing a score of 93.7%. To compare with existing approaches, we present experimental results from previous research, an ablation study of the proposed method, and results based on the high-pass filter used in the Fourier mask.

关键词： Texture image Defect recognition denoising autoencoder Fourier transform Image reconstruction

来源：评论

学校读者我要写书评

暂无评论

Infrared Dim and Small Target Detection Based on denoising autoencoder Network

引用

MOBILE NETWORKS & APPLICATIONS 2020年第4期25卷 1469-1483页

作者： Shi, Manshu Wang, Huan Nanjing Univ Sci & Technol Nanjing Peoples R China

The method of infrared small target detection is a crucial technology for infrared early-warning tasks, infrared imaging guidance, and large field of view target monitoring, and it is very important for certain early-warning tasks. In this paper, we propose an end-to-end infrared small target detection model (called CDAE) based on denoising autoencoder network and convolutional neural network, which treats small targets as "noise" in infrared images and transforms small target detection tasks into denoising problems. In addition, we use the perceptual loss to solve the problem of background texture feature loss in the encoding process, and propose the structural loss to make up for the perceptual loss defect in which small targets appear. We compare ten methods on six sequences and one single-frame dataset. Experimental results show that our method obtains the highest SCRG value on four sequences and the highest BSF value on six sequences. From the ROC curve, we can see that our method achieves the best results in all test sets.

关键词： Small target detection Infrared images denoising autoencoder End-to-end deep model Image gradient

来源：评论

学校读者我要写书评

暂无评论

Towards the Analysis of Regularized denoising autoencoder for Biosignal Processing: Lasso Versus Ridge Norms

引用

WIRELESS PERSONAL COMMUNICATIONS 2024年第1期134卷 319-338页

作者： Dasan, Evangelin Jeyabalan, Nelson Samuel Jebastin Unnamalai Inst Technol Dept Comp Sci & Engn Kovilpatti 628502 Tamil Nadu India Annamalai Univ Dept Zool Chidambaram 608002 Tamil Nadu India

The use of Internet of Things (IoT) that integrate smart bio sensor devices to the internet and shows individual health in real time. Healthcare organizations gain measurable insights into their most demanding problems like chronic diseases which demands long term monitoring. Various types of nano-sensors like Ingestible embedded in pills, Blood sampling sensor and tissue sensors are used in Healthcare IoT. Such implantable device collects, process and sends the vital signs called biosignals from particular organ of the human body where it has been implanted or fixed throughout the day to remote clinician. Such prolonged monitoring may weaken the battery power of nano-sensors. Since nano-sensors are miniaturized in nature and completely relies on its battery, energy awareness is incorporated in this paradigm that can help to avoid unnecessary energy consumption. This is achieved by data compression scheme. As the nano-sensors are light weight devices the designed algorithm should be low complex as well as efficient. As well, the signal acquired through this wireless sensor device are prone to be contaminated with noises because of the wearer's movements. In this study, regularized denoising autoencoder (DAE) has been employed to compress and recover the signal from its noisy version. Lasso (Least Absolute Shrinkage and Selection Operator) and Ridge regularization concepts are used and contrasted in this article. The cost function now includes these penalty clauses to address the overfitting problem. The experimental findings demonstrate that LASSO Norm has outperformed over RIDGE in 18% for ECG, 57% for EMG & 31% for EEG signal with respect to Quality Score. The datasets used in this investigation were taken from a database that was open to the public for testing.

关键词： Internet of Things Wireless nano-sensor device denoising autoencoder Biosignals Lasso Ridge

来源：评论

学校读者我要写书评

暂无评论

Automatic EEG eyeblink artefact identification and removal technique using independent component analysis in combination with support vector machines and denoising autoencoder

引用

IET SIGNAL PROCESSING 2020年第6期14卷 396-405页

作者： Phadikar, Souvik Sinha, Nidul Ghosh, Rajdeep NIT Silchar Dept Elect Engn Silchar Assam India Gauhati Univ Dept Informat Technol Gauhati Assam India

This study proposes a novel combination of independent component analysis (ICA) in conjunction with support vector machine (SVM) and denoising autoencoder (DA), for the first time, for removal of eyeblink artefacts from the corrupted electroencephalography (EEG). At first the eyeblink corrupted EEG signals are decomposed into independent components (ICs) using ICA, the corrupted-ICs are then identified using SVM as a classifier. From the corrupted-ICs, the artefacted segment is identified with a second SVM classifier and corrected by the pre-trained DA. Finally, inverse-ICA operation is applied on the remaining ICs and the corrected ICs to obtain the artefact-free EEG signal. The proposed methodology modifies only the portion corrupted with artefacts, and does not alter the uncorrupted part, thereby preserving the neural information in the original EEG. The proposed methodology was implemented to remove eyeblinks from the EEG data collected from the publicly available EEGLab data set. The results reveal that the proposed methodology is superior to the other recently reported methods in terms of the mutual information and average correlation coefficient. Further, the proposed method is automatic and does not require any intervention of the operator, whereas the other methods require intervention of the user.

关键词： electroencephalography medical signal processing support vector machines independent component analysis corrected ICs artefact-free independent component analysis support vector machines denoising autoencoder support vector machine eyeblink artefacts corrupted electroencephalography corrupted-ICs artefacted segment SVM classifier inverse-ICA operation remaining ICs

来源：评论

学校读者我要写书评

暂无评论

uDAS: An Untied denoising autoencoder With Sparsity for Spectral Unmixing

引用

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2019年第3期57卷 1698-1712页

作者： Qu, Ying Qi, Hairong Univ Tennessee Adv Imaging & Collaborat Informat Proc Grp Dept Elect Engn & Comp Sci Knoxville TN 37996 USA

Linear spectral unmixing is the practice of decomposing the mixed pixel into a linear combination of the constituent endmembers and the estimated abundances. This paper focuses on unsupervised spectral unmixing where the endmembers are unknown a priori. Conventional approaches use either geometrical-or statistical-based approaches. In this paper, we address the challenges of spectral unmixing with unsupervised deep learning models, in specific, the autoencoder models, where the decoder serves as the endmembers and the hidden layer output serves as the abundances. In several recent attempts, part-based autoencoders have been designed to solve the unsupervised spectral unmixing problem. However, the performance has not been satisfactory. In this paper, we first discuss some important findings we make on issues with part-based autoencoders. By proof of counterexample, we show that all existing part-based autoencoder networks with nonnegative and tied encoder and decoder are inherently defective by making these inappropriate assumptions on the network structure. As a result, they are not suitable for solving the spectral unmixing problem. We propose a so-called untied denoising autoencoder with sparsity, in which the encoder and decoder of the network are independent, and only the decoder of the network is enforced to be nonnegative. Furthermore, we make two critical additions to the network design. First, since denoising is an essential step for spectral unmixing, we propose to incorporate the denoising capacity into the network optimization in the format of a denoising constraint rather than cascading another denoising preprocessor in order to avoid the introduction of additional reconstruction error. Second, to be more robust to the inaccurate estimation of a number of endmembers, we adopt an l(21)-norm on the encoder of the network to reduce the redundant endmembers while decreasing the reconstruction error simultaneously. The experimental results demonstrate that t

关键词： Deep learning denoising autoencoder hyperspectral image (HSI) spectral unmixing

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

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

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：