2022 International Conference on Advanced Technologies for Communications (ATC)最新文献_第5页

A 1.9 µW 127 n V/ √Hz Bio Chopper Amplifier Using a Noise-Efficient Common Mode Cancelation Loop 一种1.9µW 127 n V/√Hz生物斩波放大器，采用低噪声共模抵消环路

2022 International Conference on Advanced Technologies for Communications (ATC) Pub Date : 2022-10-20 DOI: 10.1109/ATC55345.2022.9942987

Xuan Thanh Pham, X. P. Tran, Khac Vu Nguyen, Van Thai Le, D. Pham, Manh Kha Hoang

{"title":"A 1.9 µW 127 n V/ √Hz Bio Chopper Amplifier Using a Noise-Efficient Common Mode Cancelation Loop","authors":"Xuan Thanh Pham, X. P. Tran, Khac Vu Nguyen, Van Thai Le, D. Pham, Manh Kha Hoang","doi":"10.1109/ATC55345.2022.9942987","DOIUrl":"https://doi.org/10.1109/ATC55345.2022.9942987","url":null,"abstract":"Electrical neural stimulation (ENS) is widely used for implantable applications to convey information to nervous tissue. However, ENS will generate large common (CM) artifacts at the electrode tissue, leading to saturate the traditional biopotential amplifier. This paper presents a low power low noise bio chopper amplifier (BiCA) for the recording of biopotential signals. The proposed noise-efficient common mode cancelation (N-CMC) loop helps BiCA handle 650 mVpp CM artifact and avoid its noise contribution. Moreover, N-CMC helps BiCA improving the signal-noise-ratio from 12.8 to 49 dB. Beside, the proposed BiCA also uses a DC servo loop (DSL) and a ripple suppression loop (RSL) to address the electrode offset (EOS) and intenal offset (VOS), respectively. The proposed BiCA implemented in a 180 nm CMOS technology occupies only 0.11 mm2, The simulation results of the BiCA show an input referred noise of 2.73 µVrms. A common-mode rejection ratio (CMRR) and a power rejection ratio (PSRR) are 133 and 129 dB, respectively, at 50 Hz. The total current consumption of BiCA is 1.9 µA from a 1 V supply.","PeriodicalId":135827,"journal":{"name":"2022 International Conference on Advanced Technologies for Communications (ATC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124000224","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Combining U-Net Auto-encoder and MUSIC Algorithm for Improving DOA Estimation Accuracy under Defects of Antenna Array 结合U-Net自编码器和MUSIC算法提高天线阵缺陷下的DOA估计精度

2022 International Conference on Advanced Technologies for Communications (ATC) Pub Date : 2022-10-20 DOI: 10.1109/ATC55345.2022.9943003

Duy T. Nguyen, Thanh-Hai Le, Van‐Phuc Hoang, Van-Sang Doan, Duy-Thang Thai

{"title":"Combining U-Net Auto-encoder and MUSIC Algorithm for Improving DOA Estimation Accuracy under Defects of Antenna Array","authors":"Duy T. Nguyen, Thanh-Hai Le, Van‐Phuc Hoang, Van-Sang Doan, Duy-Thang Thai","doi":"10.1109/ATC55345.2022.9943003","DOIUrl":"https://doi.org/10.1109/ATC55345.2022.9943003","url":null,"abstract":"Direction of arrival (DOA) estimation plays a crucial role in radio signal surveillance and reconnaissance systems because it provides spatial information to localize radiated signal sources. Conventional DOA estimation algorithms, such as multiple signal classification (MUSIC) and estimation of signal parameters via rotational invariant technique (ESPRIT), are very sensitive to defects of antenna arrays that reduce the accuracy of estimated DOA in real applications. To mitigate this issue, an auto-encoder based on U-Net is proposed to transfer the imperfect covariance matrix to a new one; then, the MUSIC algorithm is applied to the new covariance matrix to estimate the DOAs of incoming signals. The proposed approach is investigated through simulation for a uniform linear array of eight elements with an inter-element space of half-wavelength. The simulation results indicate that our proposed method achieves a good performance in terms of DOA estimation accuracy. In comparison, the proposed model has outperformed the other models, such as conventional MUSIC, ESPRIT, and two other deep neural networks.","PeriodicalId":135827,"journal":{"name":"2022 International Conference on Advanced Technologies for Communications (ATC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124017281","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Applying Dynamic Threshold in SDN to Detect DDoS Attacks SDN动态阈值检测DDoS攻击

2022 International Conference on Advanced Technologies for Communications (ATC) Pub Date : 2022-10-20 DOI: 10.1109/ATC55345.2022.9943031

Nhat Do Van, Luong Duc Huy, Can Quang Truong, Bùi Trung Ninh, Dinh Thi Thai Mai

引用次数: 0

Micro-Doppler signatures based human activity classification using Dense-Inception Neural Network 利用密集感知神经网络进行基于微多普勒特征的人类活动分类

2022 International Conference on Advanced Technologies for Communications (ATC) Pub Date : 2022-10-20 DOI: 10.1109/ATC55345.2022.9943046

N. Nguyen, MinhNghia Pham, Vannhu Le, Dung DuongQuoc, Van-Sang Doan

引用次数: 0

MO-DLSCA: Deep Learning Based Non-profiled Side Channel Analysis Using Multi-output Neural Networks MO-DLSCA:基于深度学习的非剖面侧信道分析，使用多输出神经网络

2022 International Conference on Advanced Technologies for Communications (ATC) Pub Date : 2022-10-20 DOI: 10.1109/ATC55345.2022.9943024

Ngoc-Tuan Dol, Phu-Cuong Le, Van‐Phuc Hoang, Van-Sang Doan, Hoai Giang Nguyen, C. Pham

{"title":"MO-DLSCA: Deep Learning Based Non-profiled Side Channel Analysis Using Multi-output Neural Networks","authors":"Ngoc-Tuan Dol, Phu-Cuong Le, Van‐Phuc Hoang, Van-Sang Doan, Hoai Giang Nguyen, C. Pham","doi":"10.1109/ATC55345.2022.9943024","DOIUrl":"https://doi.org/10.1109/ATC55345.2022.9943024","url":null,"abstract":"Differential Deep Learning Analysis (DDLA) is the first side-channel analysis (SCA) attack using deep learning (DL) in non-profiled scenarios. However, DDLA requires many training processes to distinguish the correct key. In this paper, we propose a novel SCA technique using multi-output multi-loss neural networks, which can predict all possible hypothesis keys simultaneously in a short time of the training process. Specifically, a multi-output classification (MOC) model and a multi-output regression (MOR) model are introduced. Especially, we first suggest using identity labeling for MOR model to determine the trend of the training metric for each hypothesis key in the non-profiled SCA scenario. As a result, the correct key can be distinguished easily. The efficiency of proposed model is clarified on different SCA-protected schemes, such as masking and combined hiding-masking countermeasure methods. Significantly, our approach remarkably outperforms the DDLA model and parallel network in terms of the execution time and the success rate. In addition, by using shared layers, the proposed model achieves a higher success rate of at least 25 % in the case of combined hiding-masking countermeasure.","PeriodicalId":135827,"journal":{"name":"2022 International Conference on Advanced Technologies for Communications (ATC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116983722","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 6

Achievable Zero-Outage Secrecy Capacity Against Eavesdroppers with Unlimited Antennas and Arbitrary Location 具有无限天线和任意位置的窃听者可实现的零中断保密能力

2022 International Conference on Advanced Technologies for Communications (ATC) Pub Date : 2022-10-20 DOI: 10.1109/ATC55345.2022.9943018

H. Ta, Thanh Lam Cao, Khuong Ho‐Van

引用次数: 0

Three-Dimensional Direction Finding of Radio Sources in Low SNR Environments 低信噪比环境下射电源三维测向

2022 International Conference on Advanced Technologies for Communications (ATC) Pub Date : 2022-10-20 DOI: 10.1109/ATC55345.2022.9942977

Manh Linh Nguyen, T. B. Nguyen, Duc Phu Phung, Van Long Do

引用次数: 2

Optimizing Power for Data Transmissions in Uplink Cell-Free Multi-ABSs Communication Systems 上行无小区多基站通信系统数据传输功率优化

2022 International Conference on Advanced Technologies for Communications (ATC) Pub Date : 2022-10-20 DOI: 10.1109/ATC55345.2022.9943027

Bui Anh Duc, T. Hoang, Nguyen Thu Phuong, X. Tran, Pham Thanh Hiep

{"title":"Optimizing Power for Data Transmissions in Uplink Cell-Free Multi-ABSs Communication Systems","authors":"Bui Anh Duc, T. Hoang, Nguyen Thu Phuong, X. Tran, Pham Thanh Hiep","doi":"10.1109/ATC55345.2022.9943027","DOIUrl":"https://doi.org/10.1109/ATC55345.2022.9943027","url":null,"abstract":"A cell-free (CF) technology is gradually asserting its outstanding advantages by many researches, and it is viewed as a viable technology for use in 6G wireless networks. In this correspondence paper, we examine the performance of uplink CF multiple aerial base stations (ABSs) communication systems where ABSs are described as unmanned aerial vehicles (DAVs) mounted base stations. ABSs are configured with multiple antennas and stochastic distribution in a specific area to serve multiple ground users simultaneously. ABSs estimate the channels during the uplink training stage and then detect data symbols based on the estimated channels. To improve the overall performance of the uplink CF multi-ABS system, the optimization method for data transmission power is proposed. Furthermore, the closed-form of uplink achievable rate is derived based on the matched filtering technique and sequence of linear programs for numerical evaluation. The proposed optimization data transmission power is evaluated while changing several system parameters, such as the number of users, the number of ABSs and pilot sequence length. Our simulation findings demonstrate that the performance of the optimized system is superior to that of the non-optimized system.","PeriodicalId":135827,"journal":{"name":"2022 International Conference on Advanced Technologies for Communications (ATC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128813908","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Data Fusion Using Independent Vector Analysis: Solutions, Challenges, and Opportunities 使用独立矢量分析的数据融合:解决方案、挑战和机遇

2022 International Conference on Advanced Technologies for Communications (ATC) Pub Date : 2022-10-20 DOI: 10.1109/ATC55345.2022.9942997

T. Adalı

引用次数: 0

Leading 5G evolution towards 6G 引领5G向6G演进

2022 International Conference on Advanced Technologies for Communications (ATC) Pub Date : 2022-10-20 DOI: 10.1109/atc55345.2022.9943009

Patrick Tsie

引用次数: 0