SoutheastCon 2017最新文献_第6页

Removal of artifacts from Hurricane Imaging Radiometer Tb images 飓风成像辐射计Tb图像中伪影的去除

SoutheastCon 2017 Pub Date : 2017-03-01 DOI: 10.1109/SECON.2017.7925284

Abdusalam Alasgah, L. Jones, M. Jacob

引用次数: 1

Development of Autonomous Quadcopter 自主四轴飞行器的发展

SoutheastCon 2017 Pub Date : 2017-03-01 DOI: 10.1109/SECON.2017.7925262

Luke Kamrath, J. Hereford

引用次数: 2

RAM Air and Wind Energy harvesting survey for Electrical Vehicles and transportation 电动汽车和交通工具的空气和风能收集调查

SoutheastCon 2017 Pub Date : 2017-03-01 DOI: 10.1109/SECON.2017.7925346

Tarek Moustafa, Wilfrido Moreno

引用次数: 2

Network science algorithms for mobile network analytics 移动网络分析的网络科学算法

SoutheastCon 2017 Pub Date : 2017-03-01 DOI: 10.1109/SECON.2017.7925320

H. Elgazzar, Adel Said Elmaghraby

引用次数: 1

Performance of feature-based texture classifiers versus Convolutional Neural Network under sparse training data 稀疏训练数据下基于特征的纹理分类器与卷积神经网络的性能比较

SoutheastCon 2017 Pub Date : 2017-03-01 DOI: 10.1109/SECON.2017.7925325

Ryan Dellana, K. Roy

引用次数: 2

Fine-grained financial news sentiment analysis 细粒度财经新闻情绪分析

SoutheastCon 2017 Pub Date : 2017-03-01 DOI: 10.1109/SECON.2017.7925378

B. Meyer, M. Bikdash, Xiangfeng Dai

{"title":"Fine-grained financial news sentiment analysis","authors":"B. Meyer, M. Bikdash, Xiangfeng Dai","doi":"10.1109/SECON.2017.7925378","DOIUrl":"https://doi.org/10.1109/SECON.2017.7925378","url":null,"abstract":"The 24-hour news cycle and barrage of online media is a constant drum beat. The flow of positive and negative news is always in flux, influencing our current perspective and reassessing our future outlook. Nowhere is this more true than in the capital markets where assets are priced and risk assessed based on future expectations. While many factors influence a trader's decision to buy or sell an asset it can be argued that the sentiment from the 24-hour news cycle greatly impacts their outlook on the future value of an asset. In this paper we propose new methods to predict the positive or negative sentiment of financial news. Our analysis has found that contemporary document level sentiment analysis methods break down at fine-grained levels. Fine-grained analysis methods are vitally important as the velocity and impact of small texts, such as tweets and news flashes, increase their influence over the decision process. Using Natural Language Processing methods we extract syntactic sentence patterns from financial news headlines. From these patterns we conduct experiments using both lexicon and machine learning sentiment analysis approaches to predict sentiment. We find that our sentiment prediction methods are able to consistently out perform lexicon methods. Our robust techniques give the financial practitioner a method to fold a fine-grained news sentiment factor into their pricing or risk prediction models.","PeriodicalId":368197,"journal":{"name":"SoutheastCon 2017","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129584279","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

Polarized beamforming for near-field wireless jamming attacks mitigation 偏振波束形成的近场无线干扰攻击缓解

SoutheastCon 2017 Pub Date : 2017-03-01 DOI: 10.1109/SECON.2017.7925291

Ali Aldarraji, Liang Hong, S. Shetty

{"title":"Polarized beamforming for near-field wireless jamming attacks mitigation","authors":"Ali Aldarraji, Liang Hong, S. Shetty","doi":"10.1109/SECON.2017.7925291","DOIUrl":"https://doi.org/10.1109/SECON.2017.7925291","url":null,"abstract":"Wireless communication systems are susceptible to jamming attacks and interference in general from other wireless signals. Beamforming is a technique that provide a scheme to counteract the impact of jamming attacks. However, for the situation when the direction of the desired signal is close in space to that of the jamming signal, the performance of the traditional anti-jamming beamformer that utilizes isotropic antenna array elements is significantly degraded because of the fact that signals are indistinguishable from interference in the space domain. Polarized beamforming has been proposed to solve this problem by filtering the interference from the desired signal in both space and polarization domain. However, existing work only considered far-field radiation region of the signal by assuming that the transmitter and the receiver are far apart while near-field wireless communications have been employed in more and more applications. In this paper, an enhanced countermeasure method for mitigating jamming attacks in the near-field radiation region has been proposed by using the polarized beamforming with a planar array. The analytical expression of the steering vectors is derived for near-field propagation. The steering vector for far-field is shown to be an approximation of that of near-field. The beamformer is designed by using the Linearly Constrained Minimum Variance (LCMV) criterion. Simulation results in terms of beam pattern and Bit Error Rate (BER) show a significant performance improvement of the proposed anti-jamming system even when the desired signals and interferences are propagating from the same direction. Simulation results also show that the proposed approach is more efficient in near-field wireless communications than the existing work that only considered far-field propagation.","PeriodicalId":368197,"journal":{"name":"SoutheastCon 2017","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128226445","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

Transient stability analysis of high frequency AC microgrids 高频交流微电网暂态稳定性分析

SoutheastCon 2017 Pub Date : 2017-03-01 DOI: 10.1109/SECON.2017.7925393

Huaxi Zheng, P. Cairoli, Rostan Rodrigues, R. Dougal, M. Ali

引用次数: 2

Classification accuracies of malaria infected cells using deep convolutional neural networks based on decompressed images 基于解压缩图像的深度卷积神经网络对疟疾感染细胞的分类准确性

SoutheastCon 2017 Pub Date : 2017-03-01 DOI: 10.1109/SECON.2017.7925268

Yuhang Dong, Zhuocheng Jiang, Hongda Shen, W. Pan

{"title":"Classification accuracies of malaria infected cells using deep convolutional neural networks based on decompressed images","authors":"Yuhang Dong, Zhuocheng Jiang, Hongda Shen, W. Pan","doi":"10.1109/SECON.2017.7925268","DOIUrl":"https://doi.org/10.1109/SECON.2017.7925268","url":null,"abstract":"In many biomedical applications, images are stored and transmitted in the form of compressed images. However, typical pattern classifiers are trained using original images. There has been little prior study on how lossily decompressed images would impact the classification performance. In a case study of automatic classification of malaria infected cells, we used decompressed cell images as the inputs to deep convolutional neural networks. We evaluated how various lossy image compression methods and varying compression ratios would impact the classification accuracies. Specifically, we compared four compression methods: lossy compression via bitplane reduction, JPEG and JPEG 2000, and sparse autoencoders. Decompressed images were fed into LeNet-5 for training and testing. Simulation results showed that for similar compression ratios, the bitplane reduction method had the lowest classification accuracy, while JPEG and JPEG 2000 methods could maintain good accuracies. In particular, JPEG 2000 decompressed images could achieve about 95% accuracy even after 30 to 1 compression. We also provide classification results based on the widely used MNIST dataset, where handwritten digits were found to be much easier to classify using decompressed images, with about 90% accuracy still achievable using only one single bitplane. As a lossy compression method, Autoencoder was also applied to the MNIST dataset, achieving about 85% accuracy with a compression ratio much higher than the other three lossy image compression methods. Autoencoders were also found to provide more scalable compression ratios, while capable of maintaining good classification accuracies.","PeriodicalId":368197,"journal":{"name":"SoutheastCon 2017","volume":"208 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133297919","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: 18

Development and assessment of a photogrammetry system for rock specimen surface characterization 用于岩石试样表面表征的摄影测量系统的开发和评估

SoutheastCon 2017 Pub Date : 2017-03-01 DOI: 10.1109/SECON.2017.7925384

J. Oglesby, N. Hudyma, Stephanie Brown, A. Bliss, Alan Harris

引用次数: 2