2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP)最新文献_第10页

UNVEILING VESTIGES OF MAN-MADE MODIFICATIONS ON MOLECULAR-BIOLOGICAL EXPERIMENT IMAGES 揭示分子生物学实验图像上人为修饰的痕迹

2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP) Pub Date : 2018-11-01 DOI: 10.1109/GlobalSIP.2018.8646594

H. Shao

引用次数: 1

Transmission Design for a Joint MIMO Radar and MU-MIMO Downlink Communication System MIMO雷达与MU-MIMO下行通信系统的传输设计

2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP) Pub Date : 2018-11-01 DOI: 10.1109/GLOBALSIP.2018.8646647

Jiawei Liu, M. Saquib

引用次数: 8

ENERGY-EFFICIENT JOINT ANTENNA AND USER SELECTION IN SINGLE-CELL MASSIVE MIMO SYSTEMS 单小区大规模mimo系统中节能联合天线与用户选择

2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP) Pub Date : 2018-11-01 DOI: 10.1109/GlobalSIP.2018.8646642

Mangqing Guo, M. C. Gursoy

引用次数: 3

Interference Statistics Approximations for Data Rate Analysis in Uplink Massive MTC 上行海量MTC中数据速率分析的干扰统计近似

2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP) Pub Date : 2018-11-01 DOI: 10.1109/GlobalSIP.2018.8646658

Sergi Liesegang, O. Muñoz, A. Pascual-Iserte

{"title":"Interference Statistics Approximations for Data Rate Analysis in Uplink Massive MTC","authors":"Sergi Liesegang, O. Muñoz, A. Pascual-Iserte","doi":"10.1109/GlobalSIP.2018.8646658","DOIUrl":"https://doi.org/10.1109/GlobalSIP.2018.8646658","url":null,"abstract":"Machine-type-communications have attracted a lot of interest in the past years. They rely on interactions between devices with no human supervision. This will help to the advent of a plethora of applications such as the Internet-of-Things. Part of the research within this field deals with coordinating the access of a large number of devices to the network, the so-called massive machine-type-communications. In this paper, we focus on the evaluation of the data rate for that scenario, based on an approximation of the statistics of the aggregated interference that depends on the sensors activity. We will consider that the sensors can be in either active or sleep mode, modeled as a Bernoulli random variable. This results in an aggregated interference that follows a discrete distribution whose computation becomes unfeasible with the number of devices. That is why two alternatives are presented to replace the original magnitude and work with an analytic closed form expression approximating the actual statistics. Our approaches are derived using the Chernoff bound and a Gaussian approximation based on Lyapunov’s central limit theorem. The average rate is found in both cases and compared with the actual values in different setups. Monte-Carlo simulations will be used for this task.","PeriodicalId":119131,"journal":{"name":"2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133767003","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

TENSOR ENSEMBLE LEARNING FOR MULTIDIMENSIONAL DATA 多维数据的张量集成学习

2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP) Pub Date : 2018-11-01 DOI: 10.1109/GlobalSIP.2018.8646694

I. Kisil, Ahmad Moniri, D. Mandic

引用次数: 4

USING DEEP CROSS MODAL HASHING AND ERROR CORRECTING CODES FOR IMPROVING THE EFFICIENCY OF ATTRIBUTE GUIDED FACIAL IMAGE RETRIEVAL 利用深度交叉模态哈希和纠错码提高属性引导人脸图像检索的效率

2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP) Pub Date : 2018-11-01 DOI: 10.1109/GLOBALSIP.2018.8646467

Veeru Talreja, Fariborz Taherkhani, M. Valenti, N. Nasrabadi

{"title":"USING DEEP CROSS MODAL HASHING AND ERROR CORRECTING CODES FOR IMPROVING THE EFFICIENCY OF ATTRIBUTE GUIDED FACIAL IMAGE RETRIEVAL","authors":"Veeru Talreja, Fariborz Taherkhani, M. Valenti, N. Nasrabadi","doi":"10.1109/GLOBALSIP.2018.8646467","DOIUrl":"https://doi.org/10.1109/GLOBALSIP.2018.8646467","url":null,"abstract":"With benefits of fast query speed and low storage cost, hashing-based image retrieval approaches have garnered considerable attention from the research community. In this paper, we propose a novel Error-Corrected Deep Cross Modal Hashing (CMH-ECC) method which uses a bitmap specifying the presence of certain facial attributes as an input query to retrieve relevant face images from the database. In this architecture, we generate compact hash codes using an end-to-end deep learning module, which effectively captures the inherent relationships between the face and attribute modality. We also integrate our deep learning module with forward error correction codes to further reduce the distance between different modalities of the same subject. Specifically, the properties of deep hashing and forward error correction codes are exploited to design a cross modal hashing framework with high retrieval performance. Experimental results using two standard datasets with facial attributes-image modalities indicate that our CMH-ECC face image retrieval model outperforms most of the current attribute-based face image retrieval approaches.","PeriodicalId":119131,"journal":{"name":"2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122026370","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: 14

QUICKEST FREEWAY ACCIDENT DETECTION UNDER UNKNOWN POST-ACCIDENT CONDITIONS 在未知事故后条件下最快的高速公路事故检测

2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP) Pub Date : 2018-11-01 DOI: 10.1109/GlobalSIP.2018.8646617

Yasitha Warahena Liyanage, Daphney-Stavroula Zois, C. Chelmis

引用次数: 6

FACE AGING AS IMAGE-TO-IMAGE TRANSLATION USING SHARED-LATENT SPACE GENERATIVE ADVERSARIAL NETWORKS 使用共享潜在空间生成对抗网络的人脸老化图像到图像的翻译

2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP) Pub Date : 2018-11-01 DOI: 10.1109/GLOBALSIP.2018.8646447

Evangelia Pantraki, Constantine Kotropoulos

{"title":"FACE AGING AS IMAGE-TO-IMAGE TRANSLATION USING SHARED-LATENT SPACE GENERATIVE ADVERSARIAL NETWORKS","authors":"Evangelia Pantraki, Constantine Kotropoulos","doi":"10.1109/GLOBALSIP.2018.8646447","DOIUrl":"https://doi.org/10.1109/GLOBALSIP.2018.8646447","url":null,"abstract":"Here, a novel approach is proposed to generate age progression (i.e., future looks) and regression (i.e., previous looks) of persons based on their face images. The proposed method addresses face aging as an unsupervised image-to-image translation problem where the goal is to translate a face image belonging to an age class to an image of a different age class. To address this problem, we resort to adversarial training and extend the UNsupervised Image-to-image Translation (UNIT) framework to multi-domain image-to-image translation, since several age classes are considered. Due to the shared-latent space constraint of UNIT, the faces belonging to each age class/domain are forced to be mapped to a shared-latent representation. Low-level features are used to perform the transitions between the domains and to generate age progressed/regressed images. In addition, the most personal and abstract features of faces are preserved. The proposed Aging-UNIT framework is compared to state-of-the-art techniques and the ground truth. Promising results are demonstrated, which are attributed to the ability of the proposed method to capture the subtle aging transitions.","PeriodicalId":119131,"journal":{"name":"2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124869069","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: 9

VECTOR APPROXIMATE MESSAGE PASSING FOR QUANTIZED COMPRESSED SENSING 矢量近似信息传递量化压缩感知

2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP) Pub Date : 2018-11-01 DOI: 10.1109/GlobalSIP.2018.8646432

Daniel Franz, V. Kuehn

引用次数: 2

OPPORTUNISTIC SPECTRUM ACCESS VIA GOOD ARM IDENTIFICATION 通过良好的手臂识别机会频谱接入

2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP) Pub Date : 2018-11-01 DOI: 10.1109/GlobalSIP.2018.8646686

Zhiyang Wang, Ziyu Ying, Cong Shen

引用次数: 3