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Efficient normalization for quantitative evaluation of the driving behavior using a gated auto-encoder Research Articles
Xin HE, Zhe ZHANG, Li XU, Jiapei YU,xinhe_ee@zju.edu.cn,xupower@zju.edu.cn
Frontiers of Information Technology & Electronic Engineering 2022, Volume 23, Issue 3, Pages 452-462 doi: 10.1631/FITEE.2000667
Keywords: Driving behavior Normalization Gated auto-encoder Quantitative evaluation
Deep 3D reconstruction: methods, data, and challenges Review Article
Caixia Liu, Dehui Kong, Shaofan Wang, Zhiyong Wang, Jinghua Li, Baocai Yin,lcxxib@emails.bjut.edu.cn,wangshaofan@bjut.edu.cn
Frontiers of Information Technology & Electronic Engineering 2021, Volume 22, Issue 5, Pages 615-766 doi: 10.1631/FITEE.2000068
Keywords: 深度学习模型;三维重建;循环神经网络;深度自编码器;生成对抗网络;卷积神经网络
Representation learning via a semi-supervised stacked distance autoencoder for image classification Research Articles
Liang Hou, Xiao-yi Luo, Zi-yang Wang, Jun Liang,jliang@zju.edu.cn
Frontiers of Information Technology & Electronic Engineering 2020, Volume 21, Issue 7, Pages 963-1118 doi: 10.1631/FITEE.1900116
Keywords: 自动编码器;图像分类;半监督学习;神经网络
Attention-based efficient robot grasp detection network Research Article
Xiaofei QIN, Wenkai HU, Chen XIAO, Changxiang HE, Songwen PEI, Xuedian ZHANG,xiaofei.qin@usst.edu.cn,obmmd_zxd@163.com
Frontiers of Information Technology & Electronic Engineering 2023, Volume 24, Issue 10, Pages 1430-1444 doi: 10.1631/FITEE.2200502
Keywords: Robot grasp detection Attention mechanism Encoder– decoder Neural network
A new anti-jamming communication technical system:pre-encoded code hopping spread spectrum
Yao Fuqiang,Zhang Yi
Strategic Study of CAE 2011, Volume 13, Issue 10, Pages 69-75
A kind of pre-encoded code hopping spread spectrum (PCHSS) communication technical system for anti-jamming communication is brought forward and researched after analyzing its necessity based on the deficiency of conventional direct-sequence spread spectrum(DSSS). The main discussions include the basic principle of PCHSS, the differences between PCHSS and self-coded spread spectrum(SCHSS), and some key techniques. The PCHSS basic performance is analyzed finally. The technical system and its basic performance have been proved in practice.
Keywords: anti-jamming communication direct-sequence spread spectrum pre-encoded code hopping spread spectrum self-coded spread spectrum
Brain Encoding and Decoding in fMRI with Bidirectional Deep Generative Models Review
Changde Du, Jinpeng Li, Lijie Huang, Huiguang He
Engineering 2019, Volume 5, Issue 5, Pages 948-953 doi: 10.1016/j.eng.2019.03.010
Brain encoding and decoding via functional magnetic resonance imaging (fMRI) are two important aspects of visual perception neuroscience. Although previous researchers have made significant advances in brain encoding and decoding models, existing methods still require improvement using advanced machine learning techniques. For example, traditional methods usually build the encoding and decoding models separately, and are prone to overfitting on a small dataset. In fact, effectively unifying the encoding and decoding procedures may allow for more accurate predictions. In this paper, we first review the existing encoding and decoding methods and discuss
the potential advantages of a "bidirectional" modeling strategy. Next, we show that there are correspondences between deep neural networks and human visual streams in terms of the architecture and computational rules Furthermore, deep generative models (e.g., variational autoencoders (VAEs) and generative adversarial networks (GANs)) have produced promising results in studies on brain encoding and decoding. Finally, we propose that the dual learning method, which was originally designed for machine translation tasks, could help to improve the performance of encoding and decoding models by leveraging large-scale unpaired data.
Keywords: Brain encoding and decoding Functional magnetic resonance imaging Deep neural networks Deep generative models Dual learning
Displacement measuring grating interferometer: a review Special Feature on Precision Measurement and Inst
Peng-cheng HU, Di CHANG, Jiu-bin TAN, Rui-tao YANG, Hong-xing YANG, Hai-jin FU
Frontiers of Information Technology & Electronic Engineering 2019, Volume 20, Issue 5, Pages 631-654 doi: 10.1631/FITEE.1800708
A grating interferometer, called the “optical encoder,” is a commonly used tool for precise displacement measurements. In contrast to a laser interferometer, a grating interferometer is insensitive to the air refractive index and can be easily applied to multi-degree-of-freedom measurements, which has made it an extensively researched and widely used device. Classified based on the measuring principle and optical configuration, a grating interferometer experiences three distinct stages of development: homodyne, heterodyne, and spatially separated heterodyne. Compared with the former two, the spatially separated heterodyne grating interferometer could achieve a better resolution with a feature of eliminating periodic nonlinear errors. Meanwhile, numerous structures of grating interferometers with a high optical fold factor, a large measurement range, good usability, and multidegree-of-freedom measurements have been investigated. The development of incremental displacement measuring grating interferometers achieved in recent years is summarized in detail, and studies on error analysis of a grating interferometer are briefly introduced.
Keywords: Grating interferometer Optical encoder Displacement measurement Precision measurement
Jing-jing Chen, Qi-rong Mao, You-cai Qin, Shuang-qing Qian, Zhi-shen Zheng,2221808071@stmail.ujs.edu.cn,mao_qr@ujs.edu.cn,2211908026@stmail.ujs.edu.cn,2211908025@stmail.ujs.edu.cn,3160602062@stmail.ujs.edu.cn
Frontiers of Information Technology & Electronic Engineering 2020, Volume 21, Issue 11, Pages 1535-1670 doi: 10.1631/FITEE.2000019
Keywords: 语音分离;生成因子;自动编码器;深度学习
Deep learning compact binary codes for fingerprint indexing None
Chao-chao BAI, Wei-qiang WANG, Tong ZHAO, Ru-xin WANG, Ming-qiang LI
Frontiers of Information Technology & Electronic Engineering 2018, Volume 19, Issue 9, Pages 1112-1123 doi: 10.1631/FITEE.1700420
With the rapid growth in fingerprint databases, it has become necessary to develop excellent fingerprint indexing to achieve efficiency and accuracy. Fingerprint indexing has been widely studied with real-valued features, but few studies focus on binary feature representation, which is more suitable to identify fingerprints efficiently in large-scale fingerprint databases. In this study, we propose a deep compact binary minutia cylinder code (DCBMCC) as an effective and discriminative feature representation for fingerprint indexing. Specifically, the minutia cylinder code (MCC), as the state-of-the-art fingerprint representation, is analyzed and its shortcomings are revealed. Accordingly, we propose a novel fingerprint indexing method based on deep neural networks to learn DCBMCC. Our novel network restricts the penultimate layer to directly output binary codes. Moreover, we incorporate independence, balance, quantization-loss-minimum, and similarity-preservation properties in this learning process. Eventually, a multi-index hashing (MIH) based fingerprint indexing scheme further speeds up the exact search in the Hamming space by building multiple hash tables on binary code substrings. Furthermore, numerous experiments on public databases show that the proposed approach is an outstanding fingerprint indexing method since it has an extremely small error rate with a very low penetration rate.
Keywords: Fingerprint indexing Minutia cylinder code Deep neural network Multi-index hashing
A Survey of Accelerator Architectures for Deep Neural Networks Review
Yiran Chen, uan Xie, Linghao Song, Fan Chen, Tianqi Tang
Engineering 2020, Volume 6, Issue 3, Pages 264-274 doi: 10.1016/j.eng.2020.01.007
Recently, due to the availability of big data and the rapid growth of computing power, artificial intelligence (AI) has regained tremendous attention and investment. Machine learning (ML) approaches have been successfully applied to solve many problems in academia and in industry. Although the explosion of big data applications is driving the development of ML, it also imposes severe challenges of data processing speed and scalability on conventional computer systems. Computing platforms that are dedicatedly designed for AI applications have been considered, ranging from a complement to von Neumann platforms to a “must-have” and standalone technical solution. These platforms, which belong to a larger category named “domain-specific computing,” focus on specific customization for AI. In this article, we focus on summarizing the recent advances in accelerator designs for deep neural networks (DNNs)—that is, DNN accelerators. We discuss various architectures that support DNN executions in terms of computing units, dataflow optimization, targeted network topologies, architectures on emerging technologies, and accelerators for emerging applications. We also provide our visions on the future trend of AI chip designs.
Keywords: Deep neural network Domain-specific architecture Accelerator
On the principles of Parsimony and Self-consistency for the emergence of intelligence Position Paper
Yi MA, Doris TSAO, Heung-Yeung SHUM
Frontiers of Information Technology & Electronic Engineering 2022, Volume 23, Issue 9, Pages 1298-1323 doi: 10.1631/FITEE.2200297
Keywords: Intelligence Parsimony Self-consistency Rate reduction Deep networks Closed-loop transcription
Recent advances in efficient computation of deep convolutional neural networks Review
Jian CHENG, Pei-song WANG, Gang LI, Qing-hao HU, Han-qing LU
Frontiers of Information Technology & Electronic Engineering 2018, Volume 19, Issue 1, Pages 64-77 doi: 10.1631/FITEE.1700789
Keywords: Deep neural networks Acceleration Compression Hardware accelerator
Attention-based encoder-decoder model for answer selection in question answering Article
Yuan-ping NIE, Yi HAN, Jiu-ming HUANG, Bo JIAO, Ai-ping LI
Frontiers of Information Technology & Electronic Engineering 2017, Volume 18, Issue 4, Pages 535-544 doi: 10.1631/FITEE.1601232
Keywords: Question answering Answer selection Attention Deep learning
A New Algorithm of Fractal Image Coding
Wang Xiuni,Jiang Wei,Wang Licun
Strategic Study of CAE 2006, Volume 8, Issue 1, Pages 54-57
Because it takes too much of time in fractal image coding, the paper analyses the factors that affect the speed of fractal image coding , and proposes a novel idea by using the reformed variance (tentatively) to improve image fractal compression performance . A theorem is proved that the IFS cannot change the image blocks' reformed variance. Moreover , it gives a novel fractal image compression method based on the reformed variance. The simulation results illuminate that the new method can run fast, at the same time it can improve the PSNR when compared with other fast algorithms.
Keywords: fractal coding image compression variance
Application of Hydraulic Pressure Sensore System on Riverb ed ErosionDepth for Real-time Monitoring
Chen Zhijian,Liu Dawei,Zhang Weiwen
Strategic Study of CAE 2007, Volume 9, Issue 5, Pages 17-21
The problem of groundsill eroding is general and important for deep-water foundation in tidal reach. Application, technical difficulty and its settlement scheme of hydraulic pressure sensors system are introduced based on expounding the essentiality of riverbed erosion for real-time monitoring within group piled foundation. Amendment of fluctuant water level is realized by setting a sensor to monitor the tide. The sensors are fixed on the side of the piles reliably and sensor signal is transmitted smoothly with single point setting method. This method shows the characteristics of real-time, continuity, long-term and high precision when applied to the Su-Tong Bridge Project. Based on the results of monitoring, the mechanism of local scour within group piled foundation is analyzed.
Keywords: hydraulic pressure sensor tidal reach group piled foundation local scour real-time monitoring
Title Author Date Type Operation
Efficient normalization for quantitative evaluation of the driving behavior using a gated auto-encoder
Xin HE, Zhe ZHANG, Li XU, Jiapei YU,xinhe_ee@zju.edu.cn,xupower@zju.edu.cn
Journal Article
Deep 3D reconstruction: methods, data, and challenges
Caixia Liu, Dehui Kong, Shaofan Wang, Zhiyong Wang, Jinghua Li, Baocai Yin,lcxxib@emails.bjut.edu.cn,wangshaofan@bjut.edu.cn
Journal Article
Representation learning via a semi-supervised stacked distance autoencoder for image classification
Liang Hou, Xiao-yi Luo, Zi-yang Wang, Jun Liang,jliang@zju.edu.cn
Journal Article
Attention-based efficient robot grasp detection network
Xiaofei QIN, Wenkai HU, Chen XIAO, Changxiang HE, Songwen PEI, Xuedian ZHANG,xiaofei.qin@usst.edu.cn,obmmd_zxd@163.com
Journal Article
A new anti-jamming communication technical system:pre-encoded code hopping spread spectrum
Yao Fuqiang,Zhang Yi
Journal Article
Brain Encoding and Decoding in fMRI with Bidirectional Deep Generative Models
Changde Du, Jinpeng Li, Lijie Huang, Huiguang He
Journal Article
Displacement measuring grating interferometer: a review
Peng-cheng HU, Di CHANG, Jiu-bin TAN, Rui-tao YANG, Hong-xing YANG, Hai-jin FU
Journal Article
Latent source-specific generative factor learning for monaural speech separation using weighted-factor autoencoder
Jing-jing Chen, Qi-rong Mao, You-cai Qin, Shuang-qing Qian, Zhi-shen Zheng,2221808071@stmail.ujs.edu.cn,mao_qr@ujs.edu.cn,2211908026@stmail.ujs.edu.cn,2211908025@stmail.ujs.edu.cn,3160602062@stmail.ujs.edu.cn
Journal Article
Deep learning compact binary codes for fingerprint indexing
Chao-chao BAI, Wei-qiang WANG, Tong ZHAO, Ru-xin WANG, Ming-qiang LI
Journal Article
A Survey of Accelerator Architectures for Deep Neural Networks
Yiran Chen, uan Xie, Linghao Song, Fan Chen, Tianqi Tang
Journal Article
On the principles of Parsimony and Self-consistency for the emergence of intelligence
Yi MA, Doris TSAO, Heung-Yeung SHUM
Journal Article
Recent advances in efficient computation of deep convolutional neural networks
Jian CHENG, Pei-song WANG, Gang LI, Qing-hao HU, Han-qing LU
Journal Article
Attention-based encoder-decoder model for answer selection in question answering
Yuan-ping NIE, Yi HAN, Jiu-ming HUANG, Bo JIAO, Ai-ping LI
Journal Article