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

筛选
英文 中文
Fast Intra size decision and mode decision algorithm for HEVC intra coding HEVC帧内编码的快速帧内大小和模式决策算法
2019 IEEE Pacific Rim Conference on Communications, Computers and Signal Processing (PACRIM) Pub Date : 2019-08-01 DOI: 10.1109/PACRIM47961.2019.8985078
Yanling Xu, C. Yu, Yueqiang Lin
{"title":"Fast Intra size decision and mode decision algorithm for HEVC intra coding","authors":"Yanling Xu, C. Yu, Yueqiang Lin","doi":"10.1109/PACRIM47961.2019.8985078","DOIUrl":"https://doi.org/10.1109/PACRIM47961.2019.8985078","url":null,"abstract":"The latest High Efficiency Video Coding (HEVC) standard could achieve much higher coding efficiency than the current mainstream video coding standards (H.264/AVC). Compared with H.264/AVC, H.265 saves 50% cost than H.264/AVC at the same quality. But compression ratio gets improved with at least 2-4 times increase in computational complexity. HEVC provides a quad-tree based coding unit (CU) block partitioning structure. The predication mode of CU has been extended from 8 to 35. To alleviate the encoder computational complexity, we proposed an algorithm based on complexity of CU and cart decision tree to determine whether the current CU should be divided into quad-tree. In the mode decision process, we reduce the number of candidate modes involved in the rough mode decision(RMD) and the rate distortion optimization (RDO) process. The experiment results demonstrate that the proposed algorithm could significantly reduce compression time with little loss.","PeriodicalId":152556,"journal":{"name":"2019 IEEE Pacific Rim Conference on Communications, Computers and Signal Processing (PACRIM)","volume":"14 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":"132008458","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: 3
Detection of Partial Task Graph Using Deep Learning 基于深度学习的部分任务图检测
2019 IEEE Pacific Rim Conference on Communications, Computers and Signal Processing (PACRIM) Pub Date : 2019-08-01 DOI: 10.1109/PACRIM47961.2019.8985098
Taiga Tamura, M. Kai
{"title":"Detection of Partial Task Graph Using Deep Learning","authors":"Taiga Tamura, M. Kai","doi":"10.1109/PACRIM47961.2019.8985098","DOIUrl":"https://doi.org/10.1109/PACRIM47961.2019.8985098","url":null,"abstract":"Task scheduling is one of the optimization methods for parallel processing of programs. Task scheduling is intended to minimize execution time by allocating processing unit called task to appropriate computational resources. This method is considered to be impractical for large scale problems with the conventional search algorithms based on branch and bound method because of its computational complexity. One of the methods to solve this problem is to partially detect task graph and hierarchically conduct partial scheduling and complete scheduling. This can reduce computational complexity. But, this also causes another problem because the computation for detecting partial task graph itself is complicated. This research aims to solve this problem by using Deep Learning for detecting partial task graphs.","PeriodicalId":152556,"journal":{"name":"2019 IEEE Pacific Rim Conference on Communications, Computers and Signal Processing (PACRIM)","volume":"32 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":"125386917","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
Mining Retail Telecommunication Data to Predict Profitability 挖掘零售电信数据预测盈利能力
2019 IEEE Pacific Rim Conference on Communications, Computers and Signal Processing (PACRIM) Pub Date : 2019-08-01 DOI: 10.1109/PACRIM47961.2019.8985083
F. Naz, F. Popowich
{"title":"Mining Retail Telecommunication Data to Predict Profitability","authors":"F. Naz, F. Popowich","doi":"10.1109/PACRIM47961.2019.8985083","DOIUrl":"https://doi.org/10.1109/PACRIM47961.2019.8985083","url":null,"abstract":"Small or early-stage businesses often require sources of equity, loans, and/or debt funding to support their growth. An important part of the documentation accompanying funding requests can be derived from analytical data associated with the business. There are numerous commercial business intelligence (BI) tools to monitor data and generate business insights. However, most of the retail entrepreneurs still use manual and/or simple techniques, having little time to dedicate to sophisticated BI tools. In this work, we consider how supervised learning models can be used for retail telecommunications businesses. Specifically, we examine how nearest neighbour techniques, feed forward artificial neural networks, Bayesian classifiers, and support vector machines can be used with retail telecommunication data. As indicated by our initial results we have been able to achieve precision of 95.5%, recall of 94.7%, and f-measure of 95.1% which demonstrates that we can categorize retail telecommunication data based on the profitability.","PeriodicalId":152556,"journal":{"name":"2019 IEEE Pacific Rim Conference on Communications, Computers and Signal Processing (PACRIM)","volume":"189 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":"124191555","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
Capacity optimization of an actual HetNet deployment with applications to 5G 实际HetNet部署的容量优化,应用到5G
2019 IEEE Pacific Rim Conference on Communications, Computers and Signal Processing (PACRIM) Pub Date : 2019-08-01 DOI: 10.1109/PACRIM47961.2019.8985092
Diego Castro-Hernandez, Amna Feroz, R. Paranjape
{"title":"Capacity optimization of an actual HetNet deployment with applications to 5G","authors":"Diego Castro-Hernandez, Amna Feroz, R. Paranjape","doi":"10.1109/PACRIM47961.2019.8985092","DOIUrl":"https://doi.org/10.1109/PACRIM47961.2019.8985092","url":null,"abstract":"Capacity optimization techniques in HetNets will be essential for the efficient deployment 5G networks. Network operators face important challenges estimating expected performance during the planning stage of the HetNet. In particular, it is difficult to accurately model and predict the performance of the network due to the complexity of actual load conditions, traffic patterns and user behavior. In this paper, we study and analyze the performance of an actual LTE-A HetNet deployment subject to different load and interference levels. As opposed to most of the published work in this area that relies on simulated results subject to simplified conditions, this work is based on measurements from a live HetNet. We study the performance of sectorization (i.e. cell splitting) and the application of interference cancellation techniques (e.g. Almost Blank Subframes - ABS) as a way to optimize network capacity. Key performance indicators such as signal-to-interference-plus-noise ratio, reference signal received power and downlink data rate are analyzed.","PeriodicalId":152556,"journal":{"name":"2019 IEEE Pacific Rim Conference on Communications, Computers and Signal Processing (PACRIM)","volume":"63 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":"126223762","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
PACRIM 2019 Committees
2019 IEEE Pacific Rim Conference on Communications, Computers and Signal Processing (PACRIM) Pub Date : 2019-08-01 DOI: 10.1109/pacrim47961.2019.8985081
{"title":"PACRIM 2019 Committees","authors":"","doi":"10.1109/pacrim47961.2019.8985081","DOIUrl":"https://doi.org/10.1109/pacrim47961.2019.8985081","url":null,"abstract":"","PeriodicalId":152556,"journal":{"name":"2019 IEEE Pacific Rim Conference on Communications, Computers and Signal Processing (PACRIM)","volume":"8 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":"114988189","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
Basic investigation of sign language motion classification by feature extraction using pre-trained network models 基于预训练网络模型特征提取的手语动作分类研究
2019 IEEE Pacific Rim Conference on Communications, Computers and Signal Processing (PACRIM) Pub Date : 2019-08-01 DOI: 10.1109/PACRIM47961.2019.8985100
Kaito Kawaguchi, Hiromitsu Nishimura, Zhizhong Wang, Hiroshi Tanaka, Eiji Ohta
{"title":"Basic investigation of sign language motion classification by feature extraction using pre-trained network models","authors":"Kaito Kawaguchi, Hiromitsu Nishimura, Zhizhong Wang, Hiroshi Tanaka, Eiji Ohta","doi":"10.1109/PACRIM47961.2019.8985100","DOIUrl":"https://doi.org/10.1109/PACRIM47961.2019.8985100","url":null,"abstract":"This paper presents a method of classifying sign language motion using the feature elements extracted by using pre-trained networks. Good results for the image recognition were obtained using this approach. Sign language motions are diverse and complex, so it is difficult to manually extract appropriate feature elements from them. Furthermore, it is not realistic to collect a lot of sign language motion data for applying to deep learning. Therefore, it is thought that the possibility of sign language recognition system will be greatly enhanced if a pre-trained network model can be used. Feature elements of 25 types of sign language motions were extracted using the pre-trained network models including AlexNet. Trained models of sign language motions were created by Long Short Time Memory (LSTM) using feature element data, and the classification performance was evaluated. The results confirmed that an average classification rate 70.6% can be obtained with feature elements using the VGG-16 network model and the trained model created by LSTM.","PeriodicalId":152556,"journal":{"name":"2019 IEEE Pacific Rim Conference on Communications, Computers and Signal Processing (PACRIM)","volume":"60 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":"114952454","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
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
相关产品
×
本文献相关产品
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信