Pub Date : 2018-11-01DOI: 10.1109/GlobalSIP.2018.8646594
H. Shao
There are always inaccurate image data, in a scientific paper, created by inappropriate post-processing operations. Hence, we propose in this paper a fast algorithm able to expose man-made invisible modifications on molecular-biological images. We designed an optimization equation to separate the approximated trend component from the input image. Then, we utilize the difference between the input and its approximated trend to bring out the discontinuities within the input image. We applied our method on a blind test image set and images extracted from papers that have been questioned by the public. The experiment results show that there indeed exist unnatural patterns on several screened images. Because screening for fabricated images on published papers is a sensitive topic, our MATLAB code will be released only after we present this paper at IEEE GlobalSIP 2018.
{"title":"UNVEILING VESTIGES OF MAN-MADE MODIFICATIONS ON MOLECULAR-BIOLOGICAL EXPERIMENT IMAGES","authors":"H. Shao","doi":"10.1109/GlobalSIP.2018.8646594","DOIUrl":"https://doi.org/10.1109/GlobalSIP.2018.8646594","url":null,"abstract":"There are always inaccurate image data, in a scientific paper, created by inappropriate post-processing operations. Hence, we propose in this paper a fast algorithm able to expose man-made invisible modifications on molecular-biological images. We designed an optimization equation to separate the approximated trend component from the input image. Then, we utilize the difference between the input and its approximated trend to bring out the discontinuities within the input image. We applied our method on a blind test image set and images extracted from papers that have been questioned by the public. The experiment results show that there indeed exist unnatural patterns on several screened images. Because screening for fabricated images on published papers is a sensitive topic, our MATLAB code will be released only after we present this paper at IEEE GlobalSIP 2018.","PeriodicalId":119131,"journal":{"name":"2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP)","volume":"47 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":"116508479","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}
Pub Date : 2018-11-01DOI: 10.1109/GLOBALSIP.2018.8646647
Jiawei Liu, M. Saquib
We study a cooperative transmission scheme for a joint multiple-input multiple-output (MIMO) radar and multi-user (MU) MIMO downlink communication system, where both systems operate on the same frequency band simultaneously. Maximization of the total weighted system mutual information or sum rate is considered with the presence of an extended target and environmental clutter. An alternating optimization based iterative algorithm is proposed to find the transmit covariance matrices for both radar and communication applications. A power allocation policy for the downlink communication is also developed through the same algorithm.
{"title":"Transmission Design for a Joint MIMO Radar and MU-MIMO Downlink Communication System","authors":"Jiawei Liu, M. Saquib","doi":"10.1109/GLOBALSIP.2018.8646647","DOIUrl":"https://doi.org/10.1109/GLOBALSIP.2018.8646647","url":null,"abstract":"We study a cooperative transmission scheme for a joint multiple-input multiple-output (MIMO) radar and multi-user (MU) MIMO downlink communication system, where both systems operate on the same frequency band simultaneously. Maximization of the total weighted system mutual information or sum rate is considered with the presence of an extended target and environmental clutter. An alternating optimization based iterative algorithm is proposed to find the transmit covariance matrices for both radar and communication applications. A power allocation policy for the downlink communication is also developed through the same algorithm.","PeriodicalId":119131,"journal":{"name":"2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP)","volume":"36 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":"115048214","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}
Pub Date : 2018-11-01DOI: 10.1109/GlobalSIP.2018.8646642
Mangqing Guo, M. C. Gursoy
An energy-efficient joint antenna and user selection algorithm in single-cell massive multiple-input multiple-output (MIMO) communication systems is proposed in this paper. The proposed algorithm involves a two-step iterative procedure. At each time, we first obtain a subset of antennas for the given set of users via bisection search and random selection, and then obtain the optimally energy efficient subset of users with the selected antennas using cross-entropy algorithm. This two-step procedure is shown to improve the energy efficiency (EE) at each iteration. Simulation results show that the EE could be improved by 71.16% with the maximum-ratio combining (MRC) receiver when the total number of users is 60.
{"title":"ENERGY-EFFICIENT JOINT ANTENNA AND USER SELECTION IN SINGLE-CELL MASSIVE MIMO SYSTEMS","authors":"Mangqing Guo, M. C. Gursoy","doi":"10.1109/GlobalSIP.2018.8646642","DOIUrl":"https://doi.org/10.1109/GlobalSIP.2018.8646642","url":null,"abstract":"An energy-efficient joint antenna and user selection algorithm in single-cell massive multiple-input multiple-output (MIMO) communication systems is proposed in this paper. The proposed algorithm involves a two-step iterative procedure. At each time, we first obtain a subset of antennas for the given set of users via bisection search and random selection, and then obtain the optimally energy efficient subset of users with the selected antennas using cross-entropy algorithm. This two-step procedure is shown to improve the energy efficiency (EE) at each iteration. Simulation results show that the EE could be improved by 71.16% with the maximum-ratio combining (MRC) receiver when the total number of users is 60.","PeriodicalId":119131,"journal":{"name":"2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP)","volume":"128 21","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134412119","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}
Pub Date : 2018-11-01DOI: 10.1109/GlobalSIP.2018.8646658
Sergi Liesegang, O. Muñoz, A. Pascual-Iserte
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.
{"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}
Pub Date : 2018-11-01DOI: 10.1109/GlobalSIP.2018.8646694
I. Kisil, Ahmad Moniri, D. Mandic
In big data applications, classical ensemble learning is typically infeasible on the raw input data and dimensionality reduction techniques are necessary. To this end, novel framework that generalises classic flat-view ensemble learning to multidimensional tensor-valued data is introduced. This is achieved by virtue of tensor decompositions, whereby the proposed method, referred to as tensor ensemble learning (TEL), decomposes every input data sample into multiple factors which allows for a flexibility in the choice of multiple learning algorithms in order to improve test performance. The TEL framework is shown to naturally compress multidimensional data in order to take advantage of the inherent multi-way data structure and exploit the benefit of ensemble learning. The proposed framework is verified through the application of Higher Order Singular Value Decomposition (HOSVD) to the ETH-80 dataset and is shown to outperform the classical ensemble learning approach of bootstrap aggregating.
{"title":"TENSOR ENSEMBLE LEARNING FOR MULTIDIMENSIONAL DATA","authors":"I. Kisil, Ahmad Moniri, D. Mandic","doi":"10.1109/GlobalSIP.2018.8646694","DOIUrl":"https://doi.org/10.1109/GlobalSIP.2018.8646694","url":null,"abstract":"In big data applications, classical ensemble learning is typically infeasible on the raw input data and dimensionality reduction techniques are necessary. To this end, novel framework that generalises classic flat-view ensemble learning to multidimensional tensor-valued data is introduced. This is achieved by virtue of tensor decompositions, whereby the proposed method, referred to as tensor ensemble learning (TEL), decomposes every input data sample into multiple factors which allows for a flexibility in the choice of multiple learning algorithms in order to improve test performance. The TEL framework is shown to naturally compress multidimensional data in order to take advantage of the inherent multi-way data structure and exploit the benefit of ensemble learning. The proposed framework is verified through the application of Higher Order Singular Value Decomposition (HOSVD) to the ETH-80 dataset and is shown to outperform the classical ensemble learning approach of bootstrap aggregating.","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":"123809612","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}
Pub Date : 2018-11-01DOI: 10.1109/GLOBALSIP.2018.8646467
Veeru Talreja, Fariborz Taherkhani, M. Valenti, N. Nasrabadi
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.
{"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}
Pub Date : 2018-11-01DOI: 10.1109/GlobalSIP.2018.8646617
Yasitha Warahena Liyanage, Daphney-Stavroula Zois, C. Chelmis
Accurate traffic accident detection is crucial to improving road safety conditions and route navigation, and to making informed decisions in urban planning among others. This paper proposes a Bayesian quickest change detection approach for accurate freeway accident detection in near–real–time based on speed sensor readings. Since post–accident conditions are hardly known, a maximum likelihood method is devised to track the relevant unknown parameters over time. Four aggregation schemes are designed to exploit the spatial correlation among sensors. Evaluation on real–world data collected from the I405 freeway in the Los Angeles County demonstrates significant gains as compared to the state–of– the–art in terms of average detection delay and probability of false alarm by up to 58.9% and 81.5%, respectively.
{"title":"QUICKEST FREEWAY ACCIDENT DETECTION UNDER UNKNOWN POST-ACCIDENT CONDITIONS","authors":"Yasitha Warahena Liyanage, Daphney-Stavroula Zois, C. Chelmis","doi":"10.1109/GlobalSIP.2018.8646617","DOIUrl":"https://doi.org/10.1109/GlobalSIP.2018.8646617","url":null,"abstract":"Accurate traffic accident detection is crucial to improving road safety conditions and route navigation, and to making informed decisions in urban planning among others. This paper proposes a Bayesian quickest change detection approach for accurate freeway accident detection in near–real–time based on speed sensor readings. Since post–accident conditions are hardly known, a maximum likelihood method is devised to track the relevant unknown parameters over time. Four aggregation schemes are designed to exploit the spatial correlation among sensors. Evaluation on real–world data collected from the I405 freeway in the Los Angeles County demonstrates significant gains as compared to the state–of– the–art in terms of average detection delay and probability of false alarm by up to 58.9% and 81.5%, respectively.","PeriodicalId":119131,"journal":{"name":"2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP)","volume":"31 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":"124864443","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}
Pub Date : 2018-11-01DOI: 10.1109/GLOBALSIP.2018.8646447
Evangelia Pantraki, Constantine Kotropoulos
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.
{"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}
Pub Date : 2018-11-01DOI: 10.1109/GlobalSIP.2018.8646432
Daniel Franz, V. Kuehn
In recent years approximate message passing algorithms have gained a lot of attention and different versions have been proposed for coping with various system models. This paper focuses on vector approximate message passing (VAMP) for generalized linear models. While this algorithm is originally derived from a message passing point of view, we will review it from an estimation theory perspective and afterwards adapt it for a quantized compressed sensing application. Finally, numerical results are presented to evaluate the performance of the algorithm.
{"title":"VECTOR APPROXIMATE MESSAGE PASSING FOR QUANTIZED COMPRESSED SENSING","authors":"Daniel Franz, V. Kuehn","doi":"10.1109/GlobalSIP.2018.8646432","DOIUrl":"https://doi.org/10.1109/GlobalSIP.2018.8646432","url":null,"abstract":"In recent years approximate message passing algorithms have gained a lot of attention and different versions have been proposed for coping with various system models. This paper focuses on vector approximate message passing (VAMP) for generalized linear models. While this algorithm is originally derived from a message passing point of view, we will review it from an estimation theory perspective and afterwards adapt it for a quantized compressed sensing application. Finally, numerical results are presented to evaluate the performance of the algorithm.","PeriodicalId":119131,"journal":{"name":"2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP)","volume":"82 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":"125223004","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}
Pub Date : 2018-11-01DOI: 10.1109/GlobalSIP.2018.8646686
Zhiyang Wang, Ziyu Ying, Cong Shen
In this work, we promote a different tool of multi-armed bandits (MAB), called arm identification, to choose a suitable channel for Opportunistic Spectrum Access (OSA) with proven accuracy while satisfying stringent constraints on delay, energy consumption, and channel switches. Noting that finding the best channel may not always be the optimal choice, we deviate from the celebrated best arm identification framework and adopt good arm identification (GAI), which results in a channel that is "good enough", but requires much less time and energy consumption under the same accuracy requirement. Robustness issues such as delayed or missing feedback are also studied under the new framework. Performance of the proposed algorithm is studied analytically and further corroborated via numerical simulations.
{"title":"OPPORTUNISTIC SPECTRUM ACCESS VIA GOOD ARM IDENTIFICATION","authors":"Zhiyang Wang, Ziyu Ying, Cong Shen","doi":"10.1109/GlobalSIP.2018.8646686","DOIUrl":"https://doi.org/10.1109/GlobalSIP.2018.8646686","url":null,"abstract":"In this work, we promote a different tool of multi-armed bandits (MAB), called arm identification, to choose a suitable channel for Opportunistic Spectrum Access (OSA) with proven accuracy while satisfying stringent constraints on delay, energy consumption, and channel switches. Noting that finding the best channel may not always be the optimal choice, we deviate from the celebrated best arm identification framework and adopt good arm identification (GAI), which results in a channel that is \"good enough\", but requires much less time and energy consumption under the same accuracy requirement. Robustness issues such as delayed or missing feedback are also studied under the new framework. Performance of the proposed algorithm is studied analytically and further corroborated via numerical simulations.","PeriodicalId":119131,"journal":{"name":"2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP)","volume":"10 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":"125943891","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}
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