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WiFi-Based Driver's Activity Monitoring with Efficient Computation of Radio-Image Features

SENSORS 2020年第5期20卷 1381-1381页

作者： Akhtar, Zain Ul Abiden Wang, Hongyu Dalian Univ Technol Fac Elect Informat & Elect Engn Dalian 116023 Peoples R China

Driver distraction and fatigue are among the leading contributing factors in various fatal accidents. Driver activity monitoring can effectively reduce the number of roadway accidents. Besides the traditional methods that rely on camera or wearable devices, wireless technology for driver's activity monitoring has emerged with remarkable attention. With substantial progress in WiFi-based device-free localization and activity recognition, radio-image features have achieved better recognition performance using the proficiency of image descriptors. The major drawback of image features is computational complexity, which increases exponentially, with the growth of irrelevant information in an image. It is still unresolved how to choose appropriate radio-image features to alleviate the expensive computational burden. This paper explores a computational efficient wireless technique that could recognize the attentive and inattentive status of a driver leveraging Channel State Information (CSI) of WiFi signals. In this novel research work, we demonstrate an efficient scheme to extract the representative features from the discriminant components of radio-images to reduce the computational cost with significant improvement in recognition accuracy. Specifically, we addressed the problem of the computational burden by efficacious use of Gabor filters with gray level statistical features. The presented low-cost solution requires neither sophisticated camera support to capture images nor any special hardware to carry with the user. This novel framework is evaluated in terms of activity recognition accuracy. To ensure the reliability of the suggested scheme, we analyzed the results by adopting different evaluation metrics. Experimental results show that the presented prototype outperforms the traditional methods with an average recognition accuracy of 93.1% in promising application scenarios. This ubiquitous model leads to improve the system performance significantly for the diverse

关键词： Channel State Information (CSI) wireless sensing activity recognition driver distraction efficient computation sparse auto-encoder

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An intelligent fault diagnosis method for an electromechanical actuator based on sparse feature and long short-term network

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MEASUREMENT SCIENCE AND TECHNOLOGY 2021年第9期32卷 095102-095102页

作者： Yang, Jing Guo, Yingqing Zhao, Wanli Northwestern Polytech Univ Sch Power & Energy Xian 710072 Peoples R China

Electromechanical actuators (EMAs), as the new generation of actuators, have an important impact on the safety of aircraft. With the development of measurement technology, a large amount of data provides a broad prospect for the data-based fault diagnosis method. However, the existence of redundant data increases the burden of software and hardware. Therefore, a semi-supervised sparse auto-encoder (SSAE) is employed to prune observed data based on sparsity analysis. Moreover, temporal and spatial relationships are explored by a multi-channel long short-term network to build a time series model, so as to perform fault detection and isolation based on the difference between its estimated and observed values. Due to its sparse feature extraction capability, the SSAE can improve the fault isolation accuracy while pruning observed data. Verification results confirm that the proposed method can effectively diagnose EMA faults.

关键词： electro-mechanical actuator sparse auto-encoder long-short memory network sparse feature fault diagnosis

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Facial Recognition using Histogram of Gabor Phase Patterns and Self Organizing Maps 11

Facial Recognition using Histogram of Gabor Phase Patterns a...

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11th International Conference on Industrial and Information Systems (ICIIS)

作者： Arya, K. V. Upadhyay, Gaurangi Upadhyay, Shambhavi Tiwari, Shailendra Sharma, Poonam ABV IIITM Gwalior Gwalior India Manipal Univ Jaipur Jaipur Rajasthan India Manipal Univ Manipal Karnataka India Thapar Univ Patiala Patiala Punjab India VNIT Nagpur Nagpur Maharashtra India

ISBN: (纸本)9781509038183

automatic person identification using face information is very challenging task in the field of computer vision. A simple and efficient face recognition approach based on Gabor phase pattern and Self Organizing Maps is presented here. Histogram of Gabor Phase Pattern is used in proposed approach for feature extraction and Self Organizing maps are used as a classifier to identify whether the given grayscale image belongs to a particular class or not. In contrast to other feature extraction techniques, faster results are obtained using Histogram of Gabor Phase Patterns as feature detection occurs automatically without the need of any training procedure. A series of extensive experiments performed on various databases on MATLAB shows that proposed approach achieved a recognition rate of 97.33%, 96.49% and 94.20% for 7 consecutive trials on ORL, YALE and JAFFE databases respectively. It is demonstrated through experimental results that the proposed approach yields significant gain in terms of computational simplicity and effective accuracy.

关键词： automatic face recognition Gabor phase pattern Histogram of Gabor Phase Patterns Self Organizing Maps sparse auto-encoder

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Knowledge discovery in databases based on deep neural networks 10

Knowledge discovery in databases based on deep neural networ...

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10th IEEE Conference on Industrial Electronics and Applications

作者： Tan, Yuanhua Zhang, Chaolin Ma, Yonglin Mao, Yici Karamay Hongyou Software Co Xinjiang 834000 Peoples R China Applicat Management Off SINOPEC IT Management Dept Beijing 100728 Peoples R China Karamay Municipal Peoples Govt Bur Informat Ind Xinjiang 834000 Peoples R China

ISBN: (纸本)9781479983896

Knowledge discovery in databases (KDD) has received great progress in recent years for the need of mining useful knowledge in the ever growing information. The advances in machine learning technologies effectively promote KDD in the procedures of feature extraction and data categorization. This paper introduces a framework that combines feature extraction and categorization of the collected data in order to recognize useful structured patterns that underlies the raw data. This frame work consists of three modules: data pre-processing module, feature extraction module, and feature classification module. We propose a four-layered deep neural network as the feature extraction architecture. Each layer is trained in an unsupervised way as one auto-encoder with sparsity constraint. We employ a softmax classifier to assign a label to the extracted feature. The supervised and unsupervised training strategies are discussed at the end of this paper to disambiguate the training procedure of the entire model.

关键词： Knowledge discovery deep neural network sparse auto-encoder softmax classification

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An Offline Text-independent Writer Identification System with SAE Feature Extraction 4

An Offline Text-independent Writer Identification System wit...

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4th IEEE International Conference on Progress in Informatics and Computing (IEEE PIC)

作者： Zhu, Yu Wang, Yining East China Univ Sci & Technol Sch Informat Sci & Engn Shanghai Peoples R China

ISBN: (纸本)9781509034840

This paper presents an offline text-independent writer identification system in a multi-language environment using sparse auto-encoder (SAE) based codebook (SAEC) and non-text-segmentation feature extraction methods. The proposed codebook is designed by SAE structure and clustering. The superior of the designed frame work is that features can be effectively extracted and without the pre-step of text segmentation. The novel codebook can deal with mixed-languages at the same time. Hand-written texts in Chinese and English are considered in this study. We use both HIT Chinese database and IAM offline English database. The classification rate achieves the challenging accuracy of 95.56% concerning top 1 and 99.17% concerning top 10 on the two mixed databases. Another interesting aspect of our study is the evaluation of the factors such as patch sizes, patch numbers and the amount of text that influence the identification results.

关键词： offline text-independent writer identification sparse auto-encoder Codebook

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Low-Resolution Person Re-identification by a Discriminative Resolution-Invariant Network 14th

Low-Resolution Person Re-identification by a Discriminative ...

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14th Chinese Conference on Biometric Recognition (CCBR)

作者： Guo, Tongtong Lai, Jianhuang Feng, Zhanxiang Chen, Zeyu Xie, Xiaohua Zheng, Weishi Sun Yat Sen Univ Sch Data & Comp Sci Guangzhou Peoples R China Sun Yat Sen Univ Sch Elect & Informat Technol Guangzhou Peoples R China Guangdong Key Lab Informat Secur Technol Guangzhou Peoples R China Minist Educ Key Lab Machine Intelligence & Adv Comp Guangzhou Peoples R China

ISBN: (纸本)9783030314569;9783030314552

Person Re-identification (re-id) needs to tackle with the problem of changing resolutions because the pedestrians from surveillance systems or public datasets have low-resolution problem (LR-REID) including low quality, blurry textures and so on, which results in a difficult challenge to extract the identity information under various resolutions. However, most existing re-id models are trained by high-resolution (HR) images, which will achieve poor performance when conducted directly on low-resolution images. In this paper, we propose a novel Discriminative Resolution-invariant Network (DRINet) to explore the subspace where LR and HR features are highly correlated and we can extract discriminant features in the commonly shared feature space. Firstly, we adopt ResNet as the backbone and impose the softmax loss together with the triplet loss to learn distinguishing features. Secondly, we impose the KL divergence loss on the backbone features to minimize the discrepancies between LR and HR features. Finally, we integrate the sparse auto-encoder (SAE) structure to find a subspace which is robust to the resolution variations. Experimental results verify the effectiveness of the DRINet in improving the LR-REID performance and the superiority of the DRINet against the state-of-the-art methods.

关键词： Person Re-identification Low resolution sparse auto-encoder KL divergence

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Extending an Anomaly Detection Benchmark with auto-encoders, Isolation Forests, and RBMs 25th

Extending an Anomaly Detection Benchmark with Auto-encoders,...

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25th International Conference on Information and Software Technologies (ICIST)

作者： Pijnenburg, Mark Kowalczyk, Wojtek Leiden Univ Leiden Inst Adv Comp Sci Niels Bohrweg 1 Leiden Netherlands Netherlands Tax & Customs Adm Utrecht Netherlands

ISBN: (纸本)9783030302757;9783030302740

In this paper, the recently published benchmark of Goldstein and Uchida [3] for unsupervised anomaly detection is extended with three anomaly detection techniques: sparse auto-encoders, Isolation Forests, and Restricted Boltzmann Machines. The underlying mechanisms of these algorithms differ substantially from the more traditional anomaly detection algorithms, currently present in the benchmark. Results show that in three of the ten data sets, the new algorithms surpass the present collection of 19 algorithms. Moreover, a relation is noted between the nature of the outliers in a data set and the performance of specific (clusters of) anomaly detection algorithms.

关键词： Anomaly detection sparse auto-encoder Isolation forest Restricted Boltzmann machine Benchmark U-index

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SAR Target Recognition Based on Deep Learning

SAR Target Recognition Based on Deep Learning

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IEEE International Conference on Data Science & Advanced Analytics

作者： Chen, Sizhe Wang, Haipeng Fudan Univ Key Lab Informat Sci Electromagnet Waves MoE Shanghai Peoples R China

ISBN: (纸本)9781479969913

Deep learning algorithms such as convolutional neural networks (CNN) have been successfully applied in computer vision. This paper attempts to adapt the optical camera-oriented CNN to its microwave counterpart, i.e. synthetic aperture radar (SAR). As a preliminary study, a single layer of convolutional neural network is used to automatically learn features from SAR images. Instead of using the classical backpropagation algorithm, the convolution kernel is trained on randomly sampled image patches using unsupervised sparse auto-encoder. After convolution and pooling, an input SAR image is then transformed into a series of feature maps. These feature maps are then used to train a final softmax classifier. Initial experiments on MSTAR public data set show that an accuracy of 90.1% can be achieved on three types of targets classification task, and an accuracy of 84.7% is achievable on ten types of targets classification task.

关键词： Convolutional Neural Network sparse auto-encoder Synthetic Aperture Radar automatic Target Recognition

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Cross-language Speech Attribute Detection and Phone Recognition for Tibetan Using Deep Learning 9

Cross-language Speech Attribute Detection and Phone Recognit...

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9th International Symposium on Chinese Spoken Language Processing (ISCSLP)

作者： Wang, Hui Zhao, Yue Xu, Yanmin Xu, Xiaona Suo, Xingmei Ji, Qiang Minzu Univ China Dept Automat Beijing Peoples R China Rensselaer Polytech Inst Troy NY 12180 USA

ISBN: (纸本)9781479942190

Articulatory features (AFs) are viewed as the universal speech attributes for cross-language speech recognition. They are usually detected using a bank of multi-layer perceptrons (MLPs) in a supervised manner. In this paper, we propose to apply the deep learning method to detect AF-based speech attributes in a semi-supervised manner for cross-language speech recognition. The experimental results on Tibetan phone recognition showed that the deep learning method can detect the AF-based speech attributes more accurately and has higher phone recognition rates than MLPs.

关键词： Cross-language speech recognition articulatory features deep learning sparse auto-encoder Tibetan language

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A Performance Comparison of auto-encoder and Its Variants for Classification 1

A Performance Comparison of Auto-Encoder and Its Variants fo...

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1st International Conference on Signals and Systems (ICSigSys)

作者： Lee, Jae-Neung Kwak, Keun-Chang Chosun Univ Dept Control & Instrumentat Engn 375 Seosuk Dong Gwangju 501759 South Korea

ISBN: (纸本)9781509067480

In this paper, we present auto-encoder (AE), stacked auto-encoder (SAE) and sparse auto-encoder (SPAE) to classify gaits of horse riding for real riding coaching. The parameters of each auto-encoder are adjusted to compare the performance. The data is collected from 16 inertial sensors attached to a motion capture suit to construct a motion database. We build the motion features as the method of gaits classification with the database. The experiment shows that the performance is 95% when applied AE. SPAE was the best in terms of time and AE was the best in performance. We can apply to coaching system by each horse gait for rider under real or horse simulator environments using the SPAE algorithm.

关键词： auto-encoder stacked auto-encoder sparse auto-encoder classification

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