{"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}
{"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}
{"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}
{"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}
{"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}
{"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}