2019 IEEE Pacific Rim Conference on Communications, Computers and Signal Processing (PACRIM)最新文献_第5页

Generation of Coherent Signals for the Verification of Signal Processing Algorithms in Radio Astronomy 射电天文学中信号处理算法验证的相干信号生成

2019 IEEE Pacific Rim Conference on Communications, Computers and Signal Processing (PACRIM) Pub Date : 2019-08-01 DOI: 10.1109/PACRIM47961.2019.8985071

T. Gunaratne

引用次数: 2

Learning from an Imbalanced and Limited Dataset and an Application to Medical Imaging 从不平衡和有限数据集学习及其在医学成像中的应用

2019 IEEE Pacific Rim Conference on Communications, Computers and Signal Processing (PACRIM) Pub Date : 2019-08-01 DOI: 10.1109/PACRIM47961.2019.8985057

Xiaoli Qin, F. Bui, Ha H. Nguyen

{"title":"Learning from an Imbalanced and Limited Dataset and an Application to Medical Imaging","authors":"Xiaoli Qin, F. Bui, Ha H. Nguyen","doi":"10.1109/PACRIM47961.2019.8985057","DOIUrl":"https://doi.org/10.1109/PACRIM47961.2019.8985057","url":null,"abstract":"Chest X-rays (CXRs) are routinely acquired in medical imaging for the purpose of diagnosing lung diseases. But for many patients, accurate and timely radiologic interpretation of the acquired CXRs is not always feasible, due to limited medical personnel and resources. A computer aided diagnosis (CAD) system based on machine learning would be an effective solution to enhance the efficiency of disease diagnosis. However, obtaining a sufficiently large-scale, balanced, and annotated dataset of CXRs for effectively training a CAD system is challenging in practice. In this paper, we present a comprehensive comparative study on learning from imbalanced and limited CXRs to detect pneumonia, tackling two main questions: (1) Is data sampling an effective method for improving the performance of learning models? (2) Are there quantifiable differences between learning models with different sampling techniques? With respect to data sampling, we investigate two general categories of techniques that modify of an imbalanced data set to deliver a balanced data distribution: (i) undersampling the majority class; and (ii) oversampling/augmentation of the minority class. With respect to learning models, we focus on Support Vector Machine (SVM) and deep convolutional neural network (CNN). Using a publicly available CXR dataset, we demonstrate that SVM and CNN learning models both exhibit improved performance, with the proper selection of the data sampling strategies.","PeriodicalId":152556,"journal":{"name":"2019 IEEE Pacific Rim Conference on Communications, Computers and Signal Processing (PACRIM)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123897225","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: 2

Analysis of lightwave system using negative dispersion fiber and high speed optical telemetry 采用负色散光纤和高速光遥测技术的光波系统分析

2019 IEEE Pacific Rim Conference on Communications, Computers and Signal Processing (PACRIM) Pub Date : 2019-08-01 DOI: 10.1109/PACRIM47961.2019.8985065

Harminder Singh, S. Rajan, Changcheng Huang, Gauravdeep Shami, M. Lyonnais, D. Fedorov, Rodney G. Wilson

{"title":"Analysis of lightwave system using negative dispersion fiber and high speed optical telemetry","authors":"Harminder Singh, S. Rajan, Changcheng Huang, Gauravdeep Shami, M. Lyonnais, D. Fedorov, Rodney G. Wilson","doi":"10.1109/PACRIM47961.2019.8985065","DOIUrl":"https://doi.org/10.1109/PACRIM47961.2019.8985065","url":null,"abstract":"High bandwidth and fast packet processing is the need for this fast-moving communication environment. Users are in consistent need of higher data transfer with minimum delay. This research paper focuses on the simulative analysis of high data rates using optical telemetry implemented using low negative dispersion optical fiber. Low powered optical signal will be analyzed for longer transmission channels having adequate frequency magnitudes. Subsequent high-efficiency network will be realized and will be compared using similar telemetry on the lightwave system. Simple light signals will be transmitted with the help of continuous wave laser generator and then these originated signals will be merged with pulse and sequence generators for fancy output. This combined signal will be transmitted through MetroCor optical fiber and an EDFA will be present at the receiver side to provide the gain for proper analysis of the received signals. To avoid any loss in the light signal, MetroCor optical fiber will be implemented which itself is a low negative dispersion optical fiber. This fiber will replace the SMF-28 in the optical network and as a result, the need for DCB’s will be eliminated. Results based on 65 Gbps data rate having transmitting power of 0 dBm will be analyzed for high-speed communication networks.","PeriodicalId":152556,"journal":{"name":"2019 IEEE Pacific Rim Conference on Communications, Computers and Signal Processing (PACRIM)","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116842386","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

Costs and Benefits of Atmospheric Correction in the "Clouds" “云”中大气校正的成本和收益

2019 IEEE Pacific Rim Conference on Communications, Computers and Signal Processing (PACRIM) Pub Date : 2019-08-01 DOI: 10.1109/PACRIM47961.2019.8985115

Julien Godding, Bing Gao, Derek Jacoby, Y. Coady

引用次数: 0

Are you sitting right?-Sitting Posture Recognition Using RF Signals 你坐对了吗?-使用射频信号识别坐姿

2019 IEEE Pacific Rim Conference on Communications, Computers and Signal Processing (PACRIM) Pub Date : 2019-08-01 DOI: 10.1109/PACRIM47961.2019.8985070

Lin Feng, Ziyi Li, Chen Liu

{"title":"Are you sitting right?-Sitting Posture Recognition Using RF Signals","authors":"Lin Feng, Ziyi Li, Chen Liu","doi":"10.1109/PACRIM47961.2019.8985070","DOIUrl":"https://doi.org/10.1109/PACRIM47961.2019.8985070","url":null,"abstract":"Today, sedentary behaviors and bad sitting postures are the main causes of modern health musculoskeletal disorders and illnesses. Previous works either used a camera to record the image or attached wearable sensors on human body to recognize sitting postures. However, video-base approaches may face privacy issue while the wearable sensor-based approaches may cause uncomfortable to the user. This paper introduces SitR, the first sitting posture recognition system using RF signals alone. We demonstrate that with just three tags pasted to one’s back, SitR can successfully recognize three habitual sitting postures. Our design exploits the correlation between the phase change of RFID tags and the sitting postures. By extracting effective features from the measured phase sequences and employing machine learning algorithm, SitR can achieve robust and high performance. We evaluated SitR through extensive experiments including 14 volunteers under 3 different scenarios. The experiment results show that SitR can recognize sitting postures with an average accuracy of 99.27%. Our system can further detect the abnormal respiration and provide sitting posture history for sedentary people.","PeriodicalId":152556,"journal":{"name":"2019 IEEE Pacific Rim Conference on Communications, Computers and Signal Processing (PACRIM)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128687640","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: 8

PACRIM 2019 Cover Page PACRIM 2019封面页

2019 IEEE Pacific Rim Conference on Communications, Computers and Signal Processing (PACRIM) Pub Date : 2019-08-01 DOI: 10.1109/pacrim47961.2019.8985051

引用次数: 0

Conditional Training Based GM and GM-OPELM Data Fusion Schemes in Wireless Sensor Networks 无线传感器网络中基于条件训练的GM和GM- opelm数据融合方案

2019 IEEE Pacific Rim Conference on Communications, Computers and Signal Processing (PACRIM) Pub Date : 2019-08-01 DOI: 10.1109/PACRIM47961.2019.8985084

Lei Yang, Qing Zhao, Y. Jing

{"title":"Conditional Training Based GM and GM-OPELM Data Fusion Schemes in Wireless Sensor Networks","authors":"Lei Yang, Qing Zhao, Y. Jing","doi":"10.1109/PACRIM47961.2019.8985084","DOIUrl":"https://doi.org/10.1109/PACRIM47961.2019.8985084","url":null,"abstract":"As a key infrastructure of Internet of Things (loT), wireless sensor networks (WSN) can be utilized in a wide range of applications. The prediction based data fusion methods provide effective tools to reduce the amount of data transmissions while maintaining prediction accuracy. Recently a grey prediction model (GM) combining optimally-pruned extreme learning machine (OPELM) data fusion method has been proposed and shown to have good performance. However, the existing GM- OPELM method performs model training and broadcasting before each prediction, resulting in high complexity and energy consumption. In this paper the conditional training based GM (CT-GM) and GM-OPELM (CT-GM-OPELM) are proposed. By introducing an error threshold, the algorithms only perform model training when the prediction error is beyond the threshold. Compared with existing GM and GM-OPELM methods, the CT- GM and CT-GM-OPELM methods not only can achieve the higher rate of acceptable prediction and better time efficiency but also has significant reduction in the energy consumption on model training and transmissions.","PeriodicalId":152556,"journal":{"name":"2019 IEEE Pacific Rim Conference on Communications, Computers and Signal Processing (PACRIM)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126021912","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: 2

Discrete Phase Shift Design for Practical Large Intelligent Surface Communication 实用大型智能地面通信的离散相移设计

2019 IEEE Pacific Rim Conference on Communications, Computers and Signal Processing (PACRIM) Pub Date : 2019-08-01 DOI: 10.1109/PACRIM47961.2019.8985103

Jindan Xu, W. Xu, A. L. Swindlehurst

引用次数: 22

FPGA-based Implementation of HOG Algorithm: Techniques and Challenges 基于fpga的HOG算法实现:技术与挑战

2019 IEEE Pacific Rim Conference on Communications, Computers and Signal Processing (PACRIM) Pub Date : 2019-08-01 DOI: 10.1109/PACRIM47961.2019.8985056

Sina Ghaffari, Parastoo Soleimani, K. F. Li, D. Capson

引用次数: 6

Hierarchical Meta-learning Models with Deep Neural Networks for Spectrum Assignment 基于深度神经网络的频谱分配层次元学习模型

2019 IEEE Pacific Rim Conference on Communications, Computers and Signal Processing (PACRIM) Pub Date : 2019-08-01 DOI: 10.1109/PACRIM47961.2019.8985087

H. Rutagemwa, K. E. Baddour, Bo Rong

引用次数: 0