2019 IEEE Global Conference on Signal and Information Processing (GlobalSIP)最新文献_第7页

Multi-Discriminator Distributed Generative Model for Multi-Layer RF Metasurface Discovery 多层射频超表面发现的多鉴别器分布式生成模型

2019 IEEE Global Conference on Signal and Information Processing (GlobalSIP) Pub Date : 2019-11-01 DOI: 10.1109/GlobalSIP45357.2019.8969135

J. Hodge, K. Mishra, A. Zaghloul

{"title":"Multi-Discriminator Distributed Generative Model for Multi-Layer RF Metasurface Discovery","authors":"J. Hodge, K. Mishra, A. Zaghloul","doi":"10.1109/GlobalSIP45357.2019.8969135","DOIUrl":"https://doi.org/10.1109/GlobalSIP45357.2019.8969135","url":null,"abstract":"Metasurface-based antenna beam control is defining a new engineering paradigm in radio-frequency applications such as communications, radar, and analog spatial signal processing. Metasurfaces are composite electromagnetic material surfaces that are made of subwavelength scattering particles, or meta-atoms, with negligible thickness and optimized to control electromagnetic waves in unprecedented fashions through modified boundary conditions. Conventional metasurface design is a tedious process that requires iteratively solving Maxwell's equations. This becomes increasingly challenging as state-of-the-art metasurfaces require complex bi-anisotropic responses over multiple layers of meta- atoms and several frequency bands. In this paper, to reduce design time and optimization overhead, we employ a multi-discriminator distributed generative adversarial network for inverse design of multi- layer metasurfaces. Unlike conventional design approaches, our proposed approach is able to jointly design multiple layers, discover new meta- atom patterns, and avoid solving Maxwell’s equations numerically or analytically. Results show that generated triple-layer meta-atoms can achieve frequency resonances within 7% of the input values.","PeriodicalId":221378,"journal":{"name":"2019 IEEE Global Conference on Signal and Information Processing (GlobalSIP)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123920576","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: 27

GMM-UBM based Person Verification using footfall signatures for Smart Home Applications 基于GMM-UBM的智能家居应用中使用行人签名的个人验证

2019 IEEE Global Conference on Signal and Information Processing (GlobalSIP) Pub Date : 2019-11-01 DOI: 10.1109/GlobalSIP45357.2019.8969215

Sahil Anchal, Bodhibrata Mukhopadhyay, Manohar Parvatini, Subrat Kar

引用次数: 5

Covariance Matrix Decomposition Using Cascade of Linear Tree Transformations 线性树变换级联的协方差矩阵分解

2019 IEEE Global Conference on Signal and Information Processing (GlobalSIP) Pub Date : 2019-11-01 DOI: 10.1109/GlobalSIP45357.2019.8969543

N. T. Khajavi, A. Kuh

引用次数: 1

Towards a Graph Signal Processing Framework for Modeling Power System Dynamics 电力系统动力学建模的图形信号处理框架

2019 IEEE Global Conference on Signal and Information Processing (GlobalSIP) Pub Date : 2019-11-01 DOI: 10.1109/GlobalSIP45357.2019.8969365

Xinyue Hu, Zhi-Li Zhang

引用次数: 1

A comparative study of motor imagery based BCI classifiers on EEG and iEEG data 基于脑电和脑电数据的运动意象脑机接口分类器的比较研究

2019 IEEE Global Conference on Signal and Information Processing (GlobalSIP) Pub Date : 2019-11-01 DOI: 10.1109/GlobalSIP45357.2019.8969540

Naresh Nagabushan, Taber Fisher, G. Malaty, M. Witcher, S. Vijayan

{"title":"A comparative study of motor imagery based BCI classifiers on EEG and iEEG data","authors":"Naresh Nagabushan, Taber Fisher, G. Malaty, M. Witcher, S. Vijayan","doi":"10.1109/GlobalSIP45357.2019.8969540","DOIUrl":"https://doi.org/10.1109/GlobalSIP45357.2019.8969540","url":null,"abstract":"There are many state-of-the-art Brain Computer Interface (BCI) classification algorithms designed to perform well when applied to signals acquired using electroencephalography (EEG). EEG has the advantage of being non-invasive in nature, easy to use, and effective in capturing signals in the mu (7-13 Hz) and beta (13-30 Hz) bands during motor imagery tasks. However, EEG recordings are more susceptible to movement artifacts and capture a lower frequency of neural activity when compared with invasive techniques such as electrocorticography (ECoG) or intracranial EEG (iEEG). In this paper, we analyze the performance of four different EEG motor imagery classification algorithms (both classical machine learning methods and deep learning-based methods) on a two-hand motor imagery task using both EEG and iEEG data sets. Using various feature visualization techniques, we provide insight into why deep learning-based classifiers designed to learn features end-to-end may perform better than the classical machine learning-based models. We also showed on average iEEG-based motor imagery BCIs, using our iEEG data set, do not perform as well as EEG-based BCIs. This work provides a starting point for the implementation of BCI applications using iEEG data.","PeriodicalId":221378,"journal":{"name":"2019 IEEE Global Conference on Signal and Information Processing (GlobalSIP)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128669254","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

Low-complexity Proximal Gauss-Newton Algorithm for Nonnegative Matrix Factorization 非负矩阵分解的低复杂度近端高斯-牛顿算法

2019 IEEE Global Conference on Signal and Information Processing (GlobalSIP) Pub Date : 2019-11-01 DOI: 10.1109/GlobalSIP45357.2019.8969492

Kejun Huang, Xiao Fu

引用次数: 8

A Block-Floating-Point Arithmetic Based FPGA Accelerator for Convolutional Neural Networks 基于块浮点算法的FPGA卷积神经网络加速器

2019 IEEE Global Conference on Signal and Information Processing (GlobalSIP) Pub Date : 2019-11-01 DOI: 10.1109/GlobalSIP45357.2019.8969292

H. Zhang, Zhenyu Liu, Guanwen Zhang, Jiwu Dai, Xiaocong Lian, W. Zhou, Xiangyang Ji

{"title":"A Block-Floating-Point Arithmetic Based FPGA Accelerator for Convolutional Neural Networks","authors":"H. Zhang, Zhenyu Liu, Guanwen Zhang, Jiwu Dai, Xiaocong Lian, W. Zhou, Xiangyang Ji","doi":"10.1109/GlobalSIP45357.2019.8969292","DOIUrl":"https://doi.org/10.1109/GlobalSIP45357.2019.8969292","url":null,"abstract":"Convolutional neural networks (CNNs) have been widely used in computer vision applications and achieved great success. However, large-scale CNN models usually consume a lot of computing and memory resources, which makes it difficult for them to be deployed on embedded devices. An efficient block-floating-point (BFP) arithmetic is proposed in this paper. compared with 32-bit floating-point arithmetic, the memory and off-chip bandwidth requirements during convolution are reduced by 50% and 72.37%, respectively. Due to the adoption of BFP arithmetic, the complex multiplication and addition operations of floating-point numbers can be replaced by the corresponding operations of fixed-point numbers, which is more efficient on hardware. A CNN model can be deployed on our accelerator with no more than 0.14% top-1 accuracy loss, and there is no need for retraining and fine-tuning. By employing a series of ping-pong memory access schemes, 2-dimensional propagate partial multiply-accumulate (PPMAC) processors, and an optimized memory system, we implemented a CNN accelerator on Xilinx VC709 evaluation board. The accelerator achieves a performance of 665.54 GOP/s and a power efficiency of 89.7 GOP/s/W under a 300 MHz working frequency, which outperforms previous FPGA based accelerators significantly.","PeriodicalId":221378,"journal":{"name":"2019 IEEE Global Conference on Signal and Information Processing (GlobalSIP)","volume":"254 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114326059","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

An Attention Based Deep Neural Network for Automatic Lexical Stress Detection 基于注意的深度神经网络自动词法重音检测

2019 IEEE Global Conference on Signal and Information Processing (GlobalSIP) Pub Date : 2019-11-01 DOI: 10.1109/GlobalSIP45357.2019.8969232

Tian Xia, Xianfeng Rui, Chien-Lin Huang, I. Chu, Shaojun Wang, Mei Han

引用次数: 1

New Filtering Approaches to Improve the Classification Capability of Resting-state fMRI Transfer Functions 提高静息态fMRI传递函数分类能力的新滤波方法

2019 IEEE Global Conference on Signal and Information Processing (GlobalSIP) Pub Date : 2019-11-01 DOI: 10.1109/GlobalSIP45357.2019.8969301

Ehsan Shahrabi Farahani, S. H. Choudhury, F. Costello, B. Goodyear, Michael R. Smith

引用次数: 0

Robust Direction of Arrival Estimation in the Presence of Array Faults using Snapshot Diversity 基于快照分集的阵列故障鲁棒到达方向估计

2019 IEEE Global Conference on Signal and Information Processing (GlobalSIP) Pub Date : 2019-11-01 DOI: 10.1109/GlobalSIP45357.2019.8969105

Gary C. F. Lee, A. Rawat, G. Wornell

引用次数: 3