2019 IEEE International Workshop on Signal Processing Systems (SiPS)最新文献_第7页

Sub-spectrogram Segmentation for Environmental Sound Classification via Convolutional Recurrent Neural Network and Score Level Fusion 基于卷积递归神经网络和评分水平融合的环境声分类亚谱图分割

2019 IEEE International Workshop on Signal Processing Systems (SiPS) Pub Date : 2019-08-16 DOI: 10.1109/SiPS47522.2019.9020418

Tianhao Qiao, Shunqing Zhang, Zhichao Zhang, Shan Cao, Shugong Xu

引用次数: 9

Neural Dynamic Successive Cancellation Flip Decoding of Polar Codes 极性码的神经动态连续对消翻转译码

2019 IEEE International Workshop on Signal Processing Systems (SiPS) Pub Date : 2019-07-26 DOI: 10.1109/SiPS47522.2019.9020513

Nghia Doan, Seyyed Ali Hashemi, Furkan Ercan, Thibaud Tonnellier, W. Gross

引用次数: 9

Accurate Congenital Heart Disease Model Generation for 3D Printing 精确的先天性心脏病模型生成3D打印

2019 IEEE International Workshop on Signal Processing Systems (SiPS) Pub Date : 2019-07-06 DOI: 10.1109/SiPS47522.2019.9020624

Xiaowei Xu, Tianchen Wang, Dewen Zeng, Yiyu Shi, Qianjun Jia, Haiyun Yuan, Meiping Huang, Zhuang Jian

{"title":"Accurate Congenital Heart Disease Model Generation for 3D Printing","authors":"Xiaowei Xu, Tianchen Wang, Dewen Zeng, Yiyu Shi, Qianjun Jia, Haiyun Yuan, Meiping Huang, Zhuang Jian","doi":"10.1109/SiPS47522.2019.9020624","DOIUrl":"https://doi.org/10.1109/SiPS47522.2019.9020624","url":null,"abstract":"3D printing has been widely adopted for clinical decision making and interventional planning of Congenital heart disease (CHD), while whole heart and great vessel segmentation is the most significant but time-consuming step in model generation for 3D printing. While various automatic whole heart and great vessel segmentation frameworks have been developed in the literature, they are ineffective when applied to medical images in CHD, which have significant variations in heart structure and great vessel connections. To address the challenge, we leverage the power of deep learning in processing regular structures and that of graph algorithms in dealing with large variations, and propose a framework that combines both for whole heart and great vessel segmentation in CHD. Particularly, we first use deep learning to segment the four chambers and myocardium followed by blood pool, where variations are usually small. We then extract the connection information and apply graph matching to determine the categories of all the vessels. Experimental results using 68 3D CT images covering 14 types of CHD show that our method can increase Dice score by 11.9% on average compared with the state-of-the-art whole heart and great vessel segmentation method in normal anatomy. The segmentation results are also printed out using 3D printers for validation.","PeriodicalId":256971,"journal":{"name":"2019 IEEE International Workshop on Signal Processing Systems (SiPS)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121122718","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

Design and Implementation of a Neural Network Based Predistorter for Enhanced Mobile Broadband 基于神经网络的增强型移动宽带预失真器的设计与实现

2019 IEEE International Workshop on Signal Processing Systems (SiPS) Pub Date : 2019-07-01 DOI: 10.1109/SiPS47522.2019.9020606

Chance Tarver, Alexios Balatsoukas-Stimming, Joseph R. Cavallaro

{"title":"Design and Implementation of a Neural Network Based Predistorter for Enhanced Mobile Broadband","authors":"Chance Tarver, Alexios Balatsoukas-Stimming, Joseph R. Cavallaro","doi":"10.1109/SiPS47522.2019.9020606","DOIUrl":"https://doi.org/10.1109/SiPS47522.2019.9020606","url":null,"abstract":"Digital predistortion is the process of using digital signal processing to correct nonlinearities caused by the analog RF front-end of a wireless transmitter. These nonlinearities contribute to adjacent channel leakage, degrade the error vector magnitude of transmitted signals, and often force the transmitter to reduce its transmission power into a more linear but less power-efficient region of the device. Most predistortion techniques are based on polynomial models with an indirect learning architecture which have been shown to be overly sensitive to noise. In this work, we use neural network based predistortion with a novel neural network training method that avoids the indirect learning architecture and that shows significant improvements in both the adjacent channel leakage ratio and error vector magnitude. Moreover, we show that, by using a neural network based predistorter, we are able to achieve a 42% reduction in latency and 9.6% increase in throughput on an FPGA accelerator with 15% fewer multiplications per sample when compared to a similarly performing memory-polynomial implementation.","PeriodicalId":256971,"journal":{"name":"2019 IEEE International Workshop on Signal Processing Systems (SiPS)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128831895","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: 19

Deep Unfolding for Communications Systems: A Survey and Some New Directions 通信系统的深度展开:综述和一些新方向

2019 IEEE International Workshop on Signal Processing Systems (SiPS) Pub Date : 2019-06-13 DOI: 10.1109/SiPS47522.2019.9020494

Alexios Balatsoukas-Stimming, Christoph Studer

引用次数: 145

Molecular Polar Belief Propagation Decoder and Successive Cancellation Decoder 分子极性信念传播解码器和逐次消去解码器

2019 IEEE International Workshop on Signal Processing Systems (SiPS) Pub Date : 2019-03-16 DOI: 10.1109/SiPS47522.2019.9020499

Zhiwei Zhong, Lulu Ge, Zaichen Zhang, X. You, Chuan Zhang

引用次数: 1