检索结果-内蒙古大学图书馆

IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Yajun Liu Yulian Ding Aimin Li Rong Fei Xie Guo Fangxiang Wu Shaanxi Key Laboratory for Network Computing and Security Technology Xi'an University of Technology Xi'an China Division of Biomedical Engineering University of Saskatchewan Saskatoon Canada Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an University of Technology Xi'an China Department of Computer Science Department of Mechanical Engineering Division of Biomedical Engineering University of Saskatchewan Saskatoon Canada

ISBN: (数字)9781665468190

ISBN: (纸本)9781665468206

PIWI-interacting RNAs (piRNAs) are a type of small non-coding RNAs which bind with the PIWI proteins to exert biological effects in various regulatory mechanisms. A growing amount of evidence reveals that exosomal piRNAs are potential biomarkers for diagnosis and treatment of complex diseases. Effective methods for the prediction of exosomal piRNAs are the foundation of piRNA functional research. In this study, we propose an end-to-end deep network for identifying exosomal piRNAs based on features learned from natural language processing (NLP) models for sequence embedding with attention mechanism. First, a benchmark dataset is constructed by processing piRNA subcellular localization annotated data and sequence data. Moreover, bagging positive unlabeled learning is applied to get the reliable negative set. Finally, we treat a piRNA sequence as a sentence and its k-mer subsequence as a token. Sequence embedding models with self-attention mechanism is designed to extract features from exosome piRNA sequences, which are used for the prediction task. Compared with three competing methods, our model achieves the best performance and reveals the key factors of exosomal piRNA sequences by the attention mechanism. Our model characterizes exosomal piRNAs and could be beneficial for researchers to investigate exosomal piRNAs’ functions.

关键词： Proteins Location awareness RNA Biological system modeling Predictive models Feature extraction Natural language processing

来源：评论

学校读者我要写书评

暂无评论

A Deep Exploration of BitLocker Encryption and security Analysis

A Deep Exploration of BitLocker Encryption and Security Anal...

引用

International Conference on communication technology (ICCT)

作者： Cheng Tan Lijun Zhang Liang Bao Science and Technology on Communication Security Laboratory Chengdu Key Lab of Information Network Security Ministry of Public Security Shanghai China

ISBN: (数字)9781728181417

ISBN: (纸本)9781728181424

Due to the popularity of Windows system, BitLocker is widely used as a built-in disk encryption tool. As a commercial application, the design of BitLocker has to consider a capability of disaster recovery, which helps a user to recover data stored on encrypted disk when a regular access is not available. In this case, it will inevitably lead to some security risks when using BitLocker. We have a deep exploration of BitLocker encryption mechanism in this paper. We present the decryption method of encrypted VMK in case of system partition encryption and non-system partition encryption, respectively. VMK is the core key in BitLocker, with which the encrypted partition or the entire disk can be further decrypted. As for security analysis on BitLocker, we firstly make a difficulty analysis of brute force cracking on BitLocker keys, and then we analyze a possible threat caused by key theft. Based on this, we propose a few countermeasures about BitLocker usage. Additionally, we give some suggestions about security enhancement of BitLocker encryption.

关键词： Encryption Password Force Partitioning algorithms Universal Serial Bus Hard disks Dictionaries

来源：评论

学校读者我要写书评

暂无评论

Parallel in-memory wireless computing

arXiv

引用

arXiv 2023年

作者： Wang, Cong Ruan, Gong-Jie Yang, Zai-Zheng Yangdong, Xing-Jian Li, Yixiang Wu, Liang Ge, Yingmeng Zhao, Yichen Pan, Chen Wei, Wei Wang, Li-Bo Cheng, Bin Zhang, Zaichen Zhang, Chuan Liang, Shi-Jun Miao, Feng Institute of Brain-inspired Intelligence National Laboratory of Solid State Microstructures School of Physics Collaborative Innovation Center of Advanced Microstructures Nanjing University Nanjing China National Mobile Communications Research Laboratory Southeast University Frontiers Science Center for Mobile Information Communication and Security Southeast University Purple Mountain Laboratories Nanjing China Institute of Interdisciplinary Physical Sciences School of Science Nanjing University of Science and Technology Nanjing China Nanjing Drum Tower Hospital The Affiliated Hospital of Nanjing University Medical School Nanjing China

Parallel wireless digital communication with ultralow power consumption is critical for emerging edge technologies such as 5G and Internet of Things. However, the physical separation between digital computing units and analogue transmission units in traditional wireless technology leads to high power consumption. Here we report a parallel in-memory wireless computing scheme. The approach combines in-memory computing with wireless communication using memristive crossbar arrays. We show that the system can be used for the radio transmission of a binary stream of 480 bits with a bit error rate of 0/480. The in-memory wireless computing uses two orders of magnitude less power than conventional technology (based on digital-to-analogue and analogue-to-digital converters). We also show that the approach can be applied to acoustic and optical wireless communications. Copyright © 2023, The Authors. All rights reserved.

关键词： Radio transmission

来源：评论

学校读者我要写书评

暂无评论

A Classification–Detection Approach of COVID-19 Based on Chest X-ray and CT by Using Keras Pre-Trained Deep Learning Models

引用

Computer Modeling in Engineering & sciences 2020年第11期125卷 579-596页

作者： Xing Deng Haijian Shao Liang Shi Xia Wang Tongling Xie School of Computer Science Jiangsu University of Science and TechnologyZhenjiang212003China School of Automation Key Laboratory of Measurement and Control for CSEMinistry of EducationSoutheast UniversityNanjing210096China School of Computer Science and Communication Engineering Jiangsu UniversityZhenjiang212003China School of Information Science and Technology Nantong UniversityNantong226019China Department of Electrical and Computer Engineering National University of Singapore117583Singapore The People’s Hospital of RUGAO Nantong226500China

The Coronavirus Disease 2019(COVID-19)is wreaking havoc around the world,bring out that the enormous pressure on national health and medical staff *** of the most effective and critical steps in the fight against COVID-19,is to examine the patient’s lungs based on the Chest X-ray and CT generated by radiation *** this paper,five keras-related deep learning models:ResNet50,InceptionResNetV2,Xception,transfer learning and pre-trained VGGNet16 is applied to formulate an classification-detection approaches of *** benchmark methods SVM(Support Vector Machine),CNN(Conventional Neural Networks)are provided to compare with the classification-detection approaches based on the performance indicators,i.e.,precision,recall,F1 scores,confusion matrix,classification accuracy and three types of AUC(Area Under Curve).The highest classification accuracy derived by classification-detection based on 5857 Chest X-rays and 767 Chest CTs are respectively 84%and 75%,which shows that the keras-related deep learning approaches facilitate accurate and effective COVID-19-assisted detection.

关键词： COVID-19 detection deep learning transfer learning pre-trained models

来源：评论

学校读者我要写书评

暂无评论

STC-IDS: Spatial-Temporal Correlation Feature Analyzing based Intrusion Detection System for Intelligent Connected Vehicles

arXiv

引用

arXiv 2022年

作者： Cheng, Pengzhou Han, Mu Li, Aoxue Zhang, Fengwei The Department of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai201100 China The School of Computer Science and Communication Engineering Jiangsu Key Laboratory of Security Technology for Industrial Cyberspace Jiangsu University Zhenjiang212013 China The School of Automotive and Traffic Engineering Jiangsu University Zhenjiang212013 China The Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518000 China

Intrusion detection is an important defensive measure for automotive communications security. Accurate frame detection models assist vehicles to avoid malicious attacks. Uncertainty and diversity regarding attack methods make this task challenging. However, the existing works have the limitation of only considering local features or the weak feature mapping of multi-features. To address these limitations, we present a novel model for automotive intrusion detection by spatial-temporal correlation features of in-vehicle communication traffic (STC-IDS). Specifically, the proposed model exploits an encoding-detection architecture. In the encoder part, spatial and temporal relations are encoded simultaneously. To strengthen the relationship between features, the attention-based convolutional network still captures spatial and channel features to increase the receptive field, while attention-LSTM builds meaningful relationships from previous time series or crucial bytes. The encoded information is then passed to detector for generating forceful spatial-temporal attention features and enabling anomaly classification. In particular, single-frame and multi-frame models are constructed to present different advantages respectively. Under automatic hyper-parameter selection based on Bayesian optimization, the model is trained to attain the best performance. Extensive empirical studies based on a real-world vehicle attack dataset demonstrate that STC-IDS has outperformed baseline methods and obtains fewer false-alarm rates while maintaining efficiency. © 2022, CC BY.

关键词： Intrusion detection

来源：评论

学校读者我要写书评

暂无评论

Fault Detection Method for Insulators Using Improved YOLOv4

Journal of Network Intelligence

引用

Journal of Network Intelligence 2022年第4期7卷 818-834页

作者： Zhao, Li-Quan Jiang, Zi-Cong Teng, Zi-Ming Jia, Yan-Fei Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education Northeast Electric Power University 168 Changchun Road Jilin132012 China Graduate school of Information Science and Electrical Engineering Kyushu University Fukuoka819-0395 Japan College of information and communication Jilin University Changchun130012 China School of Electrical and Information Engineering Beihua University Jilin132013 China

A defective insulator detection method based on improved You Only Look Once version 4 (YOLOv4) is proposed to improve the precision of defective insulator detection. This method designs a dual-branch attention block by simulating the attention process of the human eye, and introduces it into the backbone of YOLOv4 to extract richer information on defective insulator features. To make the output feature map of shallow layer network fuse more different scale features, a path aggregation network has been designed. To improve detection accuracy, the spatial pyramid pooling has been replaced by atrous spatial pyramid pooling. A dynamic non-maximum suppression method is proposed to improve defective insulator detection accuracy. It employs an adaptive threshold control strategy and weighted operation to full use of the predicted box. We compare our method with defective insulator detection methods based on YOLO and R-CNN. Our simulation results show that the proposed method is more accurate than defective insulator detection methods based on YOLO, and has a faster detection speed than methods based on R-CNN. Compared with the defective insulator detection method based on R-CNN that has the highest mean average precision, the mean average precision of the proposed method is lower, but its detection speed is faster. Compared with YOLOv4, its detection speed is slower, but the mean average precision is higher. The proposed method strikes a satisfactory tradeoff between detection speed and mean average precision. © 2022, Taiwan Ubiquitous information CO LTD. All rights reserved.

关键词： Object detection

来源：评论

学校读者我要写书评

暂无评论

Compacting deep neural networks for internet of things: Methods and applications

arXiv

引用

arXiv 2021年

作者： Zhang, Ke Ying, Hanbo Dai, Hong-Ning Li, Lin Peng, Yuangyuang Guo, Keyi Yu, Hongfang School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Science and Technology on Electronic Information Control Laboratory Chengdu China School of Communication and Information Engineering University of Electronic Science and Technology of China Chengdu China Faculty of Information Technology Macau University of Science and Technology China Courant Institute of Mathematical Science New York University New York United States

Deep Neural Networks (DNNs) have shown great success in completing complex tasks. However, DNNs inevitably bring high computational cost and storage consumption due to the complexity of hierarchical structures, thereby hindering their wide deployment in Internet-of-Things (IoT) devices, which have limited computational capability and storage capacity. Therefore, it is a necessity to investigate the technologies to compact DNNs. Despite tremendous advances in compacting DNNs, few surveys summarize compacting-DNNs technologies, especially for IoT applications. Hence, this paper presents a comprehensive study on compacting-DNNs technologies. We categorize compacting-DNNs technologies into three major types: 1) network model compression, 2) Knowledge Distillation (KD), 3) modification of network structures. We also elaborate on the diversity of these approaches and make side-by-side comparisons. Moreover, we discuss the applications of compacted DNNs in various IoT applications and outline future *** Codes 68T07 © 2021, CC BY-NC-SA.

关键词： Internet of things

来源：评论

学校读者我要写书评

暂无评论

Efficient Channel Estimation for Millimeter Wave and Terahertz Systems Enabled by Integrated Super-resolution Sensing and communication

arXiv

引用

arXiv 2024年

作者： Xu, Jingran Wang, Huizhi Zeng, Yong Xu, Xiaoli Wu, Qingqing Yang, Fei Chen, Yan Jamalipour, Abbas The National Mobile Communications Research Laboratory Frontiers Science Center for Mobile Information Communication and Security Southeast University Nanjing210096 China The Purple Mountain Laboratories Nanjing211111 China Department of Electronic Engineering Shanghai Jiao Tong University Shanghai200240 China The Wireless Technology Lab. 2012 Lab Shanghai Huawei Technologies Co. Ltd. Shanghai201206 China The School of Electrical and Computer Engineering The University of Sydney CamperdownNSW2006 Australia

Integrated super-resolution sensing and communication (ISSAC) has emerged as a promising technology to achieve extremely high precision sensing for those key parameters, such as the angles of the sensing targets. In this paper, we propose an efficient channel estimation scheme enabled by ISSAC for millimeter wave (mmWave) and TeraHertz (THz) systems with a hybrid analog/digital beamforming architecture, where both the pilot overhead and the cost of radio frequency (RF) chains are significantly reduced. The key idea is to exploit the fact that subspace-based super-resolution algorithms such as multiple signal classification (MUSIC) can estimate channel parameters accurately without requiring dedicate a priori known pilots. In particular, the proposed method consists of two stages. First, the angles of the multi-path channel components are estimated in a pilot-free manner during the transmission of data symbols. Second, the multi-path channel coefficients are estimated with very few pilots. Compared to conventional channel estimation schemes that rely solely on channel training, our approach requires the estimation of much fewer parameters in the second stage. Furthermore, with channel multi-path angles obtained, the beamforming gain can be achieved when pilots are sent to estimate the channel path gains. To comprehensively investigate the performance of the proposed scheme, we consider both the basic line-of-sight (LoS) channels and more general multipath channels. We compare the performance of the minimum mean square error (MMSE) of channel estimation and the resulting beamforming gains of our proposed scheme with the traditional scheme that rely exclusively on channel training. It is demonstrated that our proposed method significantly outperforms the benchmarking scheme. Simulation results are presented to validate our theoretical findings. Copyright © 2024, The Authors. All rights reserved.

关键词： Mean square error

来源：评论

学校读者我要写书评

暂无评论

SAR Image Processing and Target Detection Based on 2D Singularity Power Spectrum in Time-frequency Domain

SAR Image Processing and Target Detection Based on 2D Singul...

引用

IEEE International Conference on Signal and Image Processing (ICSIP)

作者： Junye Li Gang Xiong Baigang Huang School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China Science and Technology on Communication Information Security Control Laboratory Jiaxing China

ISBN: (数字)9781728168968

ISBN: (纸本)9781728168975

A method for SAR image processing and target detection based on two-dimensional singularity power spectrum (2D-SPS) in time-frequency domain is proposed in this paper. Firstly, the two-dimensional pseudo Wigner-Ville distribution (2D-PWVD) is carried out on the SAR image. Then, the two-dimensional singularity power spectrum estimation is performed on the local delay components of 2D-PWVD to obtain the three-dimensional singularity power spectral SAR (SPS-SAR) functions. Furthermore, a novel SAR target detection method based on SPS-SAR is introduced. The proposed methodology is tested on SAR images, both with and without weak targets, from the OpenSAR dataset. Experiment results show that the proposed method performs better than traditional method based on fractal theory, and can achieve higher detection probability in the complex scene with low SNR. It can provide more details for SAR image analysis and offer a new idea for SAR target detection.

关键词： Time-frequency analysis Two dimensional displays Object detection Radar polarimetry Fractals Spectral analysis Signal to noise ratio

来源：评论

学校读者我要写书评

暂无评论

Practical security analysis of a continuous-variable quantum random-number generator with a noisy local oscillator

引用

Physical Review A 2020年第1期102卷 012422-012422页

作者： Weinan Huang Yichen Zhang Ziyong Zheng Yang Li Bingjie Xu Song Yu State Key Laboratory of Information Photonics and Optical Communications Beijing University of Posts and Telecommunications Beijing 100876 China Science and Technology on Security Communication Laboratory Institute of Southwestern Communication Chengdu 610041 China

A quantum random-number generator (QRNG) can theoretically generate unpredictable random numbers with perfect devices and is an ideal and secure source of random numbers for cryptography. However, the practical implementations always contain imperfections, which will greatly influence the randomness of the final output and even open loopholes to eavesdroppers. Recently, Thewes et al. experimentally demonstrated a continuous-variable eavesdropping attack, based on heterodyne detection, on a trusted continuous-variable QRNG in Phys. Rev. A 100, 052318 (2019), yet like in many other practical continuous-variable QRNG studies, they always supposed the local oscillator was stable and ignored its fluctuation, which might lead to security threats such as wavelength attack. In this work, based on the theory of the conditional min-entropy, imperfections of the practical security of continuous-variable QRNGs are systematically analyzed, especially the local oscillator fluctuation under imbalanced homodyne detection. Experiments of a practical QRNG based on vacuum fluctuation are demonstrated to show the influence of local oscillator fluctuation on the total measurement noise variances and the practical conditional min-entropy with beam splitters of different transmittances. Moreover, a local oscillator monitoring method is proposed for the practical continuous-variable QRNG, which can be used to calibrate the practical conditional min-entropy.

关键词： Quantum cryptography Quantum information processing Quantum information processing with continuous variables Quantum optics

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：