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	Breaking the hardness assumption and IND-CPA security of HQC submitted to NIST PQC project 期刊论文 OAI收割 IET INFORMATION SECURITY, 2020, 卷号: 14, 期号: 3, 页码: 313-320 作者: Liu, Zhen; Pan, Yanbin; Xie, Tianyuan \| 收藏 \| 浏览/下载：46/0 \| 提交时间：2020/10/12 decoding public key cryptography quantum cryptography computational complexity optimisation cyclic codes IND-CPA security NIST PQC project hamming quasicyclic cryptosystem code-based key encapsulation mechanism NIST standardisation process public-key encryption scheme IND-CCA2 secure KEM revised scheme HQC- beta HQC cryptosystem s-DQCSD problem s-decision quasi-cyclic syndrome decoding plaintext attack
	A Decoding Scheme for Incomplete Motor Imagery EEG With Deep Belief Network 期刊论文 OAI收割 FRONTIERS IN NEUROSCIENCE, 2018, 卷号: 12, 页码: 17 作者: Chu, Yaqi; Zhao, Xingang; Zou, Yijun; Xu, Weiliang; Han, Jianda \| 收藏 \| 浏览/下载：28/0 \| 提交时间：2021/02/02 brain-computer interface decoding scheme incomplete motor imagery EEG power spectral density deep belief network
	A Decoding Scheme for Incomplete Motor Imagery EEG With Deep Belief Network 期刊论文 OAI收割 FRONTIERS IN NEUROSCIENCE, 2018, 卷号: 12, 页码: 17 作者: Chu, Yaqi; Zhao, Xingang; Zou, Yijun; Xu, Weiliang; Han, Jianda \| 收藏 \| 浏览/下载：35/0 \| 提交时间：2021/02/02 brain-computer interface decoding scheme incomplete motor imagery EEG power spectral density deep belief network
	A Decoding Scheme for Incomplete Motor Imagery EEG With Deep Belief Network 期刊论文 OAI收割 FRONTIERS IN NEUROSCIENCE, 2018, 卷号: 12, 页码: 1-17 作者: Zou YJ(邹宜君); Zhao XG(赵新刚); Zhao YW(赵忆文); Xu WL(徐卫良); Han JD(韩建达) \| 收藏 \| 浏览/下载：39/0 \| 提交时间：2018/10/22 brain-computer interface decoding scheme incomplete motor imagery EEG power spectral density deep belief network
	Study of unequal error protection method based on JPEG2000 for surveyingmapping image (EI CONFERENCE) 会议论文 OAI收割 International Conference on Computer Science and Software Engineering, CSSE 2008, December 12, 2008 - December 14, 2008, Wuhan, Hubei, China Yao Q.; Cao M. 收藏 \| 浏览/下载：165/0 \| 提交时间：2013/03/25 On aviation mapping compression image maybe not decode because of wireless transmission interference. UEP(Unequal Error Protection) scheme is put forward about layer and block structure of JPEG2000 image compression method flexibly enhances local reconfigurable image's quality which is in favor of identifier in the region of interest. 2008 IEEE. and realizes three degrees UEP of data block data header image data via various degrees correction-coding under the condition of obstruct naked and shade. BCH RS and Hamming coding are simulated and image decoding success rate and image's quality are analyzed according to different coding and ROI (Region of Interest) in the Gaussian white noise model and rayleigh fading model. Results shows that the method we putted forward roughly improves decoding success rate at the same time almost not increasing bandwidth
	Lossless wavelet compression on medical image (EI CONFERENCE) 会议论文 OAI收割 4th International Conference on Photonics and Imaging in Biology and Medicine, September 3, 2005 - September 6, 2005, Tianjin, China 作者: Liu H.; Liu H.; Liu H. 收藏 \| 浏览/下载：42/0 \| 提交时间：2013/03/25 An increasing number of medical imagery is created directly in digital form. Such as Clinical image Archiving and Communication Systems (PACS). as well as telemedicine networks require the storage and transmission of this huge amount of medical image data. Efficient compression of these data is crucial. Several lossless and lossy techniques for the compression of the data have been proposed. Lossless techniques allow exact reconstruction of the original imagery while lossy techniques aim to achieve high compression ratios by allowing some acceptable degradation in the image. Lossless compression does not degrade the image thus facilitating accurate diagnosis of course at the expense of higher bit rates i.e. lower compression ratios. Various methods both for lossy (irreversible) and lossless (reversible) image compression are proposed in the literature. The recent advances in the lossy compression techniques include different methods such as vector quantization wavelet coding neural networks and fractal coding. Although these methods can achieve high compression ratios (of the order 50:1 or even more) they do not allow reconstructing exactly the original version of the input data. Lossless compression techniques permit the perfect reconstruction of the original image but the achievable compression ratios are only of the order 2:1 up to 4:1. In our paper we use a kind of lifting scheme to generate truly loss-less non-linear integer-to-integer wavelet transforms. At the same time we exploit the coding algorithm producing an embedded code has the property that the bits in the bit stream are generated in order of importance so that all the low rate codes are included at the beginning of the bit stream. Typically the encoding process stops when the target bit rate is met. Similarly the decoder can interrupt the decoding process at any point in the bil stream and still reconstruct the image. Therefore a compression scheme generating an embedded code can start sending over the network the coarser version of the image first and continues with the progressive transmission of the refinement details. Experimental results show that our method can get a perfect performance in compression ratio and reconstructive image.