Proceedings of the 2018 Conference on Research in Adaptive and Convergent Systems最新文献_第7页

Phoneme segmentation using deep learning for speech synthesis 语音合成中使用深度学习的音位分割

Proceedings of the 2018 Conference on Research in Adaptive and Convergent Systems Pub Date : 2018-10-09 DOI: 10.1145/3264746.3264801

Young Han Lee, Jong-Yeol Yang, C. Cho, Hyedong Jung

引用次数: 3

Proceedings of the 2018 Conference on Research in Adaptive and Convergent Systems 2018年自适应与收敛系统研究会议论文集

Proceedings of the 2018 Conference on Research in Adaptive and Convergent Systems Pub Date : 2018-10-09 DOI: 10.1145/3264746

引用次数: 3

Development of automated 3D knee bone segmentation with inhomogeneity correction for deformable approach in magnetic resonance imaging 磁共振成像变形方法中不均匀性校正自动三维膝关节分割的发展

Proceedings of the 2018 Conference on Research in Adaptive and Convergent Systems Pub Date : 2018-10-09 DOI: 10.1145/3264746.3264776

Dongyoun Kim, Jiyoung Lee, J. Yoon, K. Lee, Kwanghee Won

{"title":"Development of automated 3D knee bone segmentation with inhomogeneity correction for deformable approach in magnetic resonance imaging","authors":"Dongyoun Kim, Jiyoung Lee, J. Yoon, K. Lee, Kwanghee Won","doi":"10.1145/3264746.3264776","DOIUrl":"https://doi.org/10.1145/3264746.3264776","url":null,"abstract":"Osteoarthritis(OA) analysis is one of essential task in health issues. 3D Magnetic Resonance Imaging (MRI) segmentation plays an important role in a highly accurate knee osteoarthritis diagnosis. 3D segmentation knee MRI is challenging task because of complex knee structure, low contrast, noise, and bias field inherent in MRI. Deformable model is one of the most intensively model-based approaches for computer-aided medical image analysis. However, most of deformable models require prior shape and training processing for segmentation [1]. In this paper, we propose a deformable model-based approach with automatic initial point selection to segment knee bones from 3D MRI containing intensity inhomogeneity. This approach does not require manual initial point selection and training phase so that large amount of human resource and time can be saved. Preprocessing performs inhomogeneity correction and extracts voxels of interest in order to prevent leakage the boundary of target objective. The proposed deformable approach is devised by modifying boundary information of a hybrid deformable model [2] to morphological operation. Automated selection of initial point is motivated by 3D multi-edge overlapping technique in the [3] method. Experimental results are demonstrated 3D model comparing with other recent methods of knee bone segmentation [27,28] and 2D slices on both synthetic image with inhomogeneity correction or not. Our approach compared against a hand-segmented ground truth from experts, we achieved an average dice similarity coefficient of 0.951, sensitivity of 0.927, specificity of 0.999, average symmetric surface distance of 1.16 mm, and root mean square symmetric surface of 2.01mm. The result shows that our proposed approach is useful performing simple and accurate bone segmentation for diagnosis.","PeriodicalId":186790,"journal":{"name":"Proceedings of the 2018 Conference on Research in Adaptive and Convergent Systems","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126959833","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

Effects of H.265 encoding on PNU and source camera identification H.265编码对PNU和源摄像机识别的影响

Proceedings of the 2018 Conference on Research in Adaptive and Convergent Systems Pub Date : 2018-10-09 DOI: 10.1145/3264746.3264770

Andrea Bruno, G. Cattaneo

{"title":"Effects of H.265 encoding on PNU and source camera identification","authors":"Andrea Bruno, G. Cattaneo","doi":"10.1145/3264746.3264770","DOIUrl":"https://doi.org/10.1145/3264746.3264770","url":null,"abstract":"Digital Videos (DVs) are a powerful mean of communication and they are spreading on Social Networks (SNs) more and more every day (i.e. ~ 100 million of hours of video viewed per day1 in 2016). H.264 AVC is a common video format used to encode videos. It has been proved to be very effective for HD videos, but when the video resolution increases (4K and 8K) the efficiency decreases. For this reason in the next years more and more Online Social Networks (OSNs) will move to the new H.265 HEVC standard[15]. It is widely known that Pixel Non Uniformity (PNU) noise can be exploited to identify the camera that acquired an image applying the method introduced by Fridrich et al. [16]. The same technique has been adapted to identify video DVs source camera. Unfortunately the noise present in a digital image heavily depends on the compression level used to store it and high (lossy) compression algorithms, can completely filter out any kind of noise. In this paper we analyze the effects of H.265 encoding on PNU noise, to prove under which conditions it still can be exploited for Source Camera Identification (SCI) for DV. We present the results of several experiments explicitly designed to point out the limits of this technique when dealing with H.265 videos.","PeriodicalId":186790,"journal":{"name":"Proceedings of the 2018 Conference on Research in Adaptive and Convergent Systems","volume":"194 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115255425","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

Autonomous configuration of network parameters in operating systems using evolutionary algorithms 使用进化算法的操作系统中网络参数的自主配置

Proceedings of the 2018 Conference on Research in Adaptive and Convergent Systems Pub Date : 2018-08-31 DOI: 10.1145/3264746.3264799

Bartosz Gembala, A. Yazidi, H. Haugerud, S. Nichele

{"title":"Autonomous configuration of network parameters in operating systems using evolutionary algorithms","authors":"Bartosz Gembala, A. Yazidi, H. Haugerud, S. Nichele","doi":"10.1145/3264746.3264799","DOIUrl":"https://doi.org/10.1145/3264746.3264799","url":null,"abstract":"By default, the Linux network stack is not configured for highspeed large file transfer. The reason behind this is to save memory resources. It is possible to tune the Linux network stack by increasing the network buffers size for high-speed networks that connect server systems in order to handle more network packets. However, there are also several other TCP/IP parameters that can be tuned in an Operating System (OS). In this paper, we leverage Genetic Algorithms (GAs) to devise a system which learns from the history of the network traffic and uses this knowledge to optimize the current performance by adjusting the parameters. This can be done for a standard Linux kernel using sysctl or /proc. For a Virtual Machine (VM), virtually any type of OS can be installed and an image can swiftly be compiled and deployed. By being a sandboxed environment, risky configurations can be tested without the danger of harming the system. Different scenarios for network parameter configurations are thoroughly tested, and an increase of up to 65% throughput speed is achieved compared to the default Linux configuration.","PeriodicalId":186790,"journal":{"name":"Proceedings of the 2018 Conference on Research in Adaptive and Convergent Systems","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128025523","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: 1