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          An Auctioning Mechanism for Green Radio

          Comaniciu, Cristina,Mandayam, Narayan B.,Poor, H. Vincent,Gorce, Jean-Marie The Korean Institute of Communications and Informa 2010 Journal of communications and networks Vol.12 No.2

          In this paper, an auctioning strategy is proposed for cellular networks that ensures net energy savings. The pricing scheme, in conjunction with a two dimensional bid structure, incentivizes cooperation at the terminal nodes for better interference management at receivers and for cooperative relaying. It is shown that, for the proposed auctioning strategy, network operators are guaranteed revenue gains, mobile nodes' dominant strategy is to bid their true valuation of their energy resources, and overall effective energy gains occur under the assumption of a reserve price for bidding. Simulation results show that significant energy savings can be achieved by employing this auctioning mechanism for a 3G cellular set-up.

        • KCI등재

          An Auctioning Mechanism for Green Radio

          Cristina Comaniciu,Narayan B. Mandayam,H. Vincent Poor,Jean-Marie Gorce 한국통신학회 2010 Journal of communications and networks Vol.12 No.2

          In this paper, an auctioning strategy is proposed for cellular networks that ensures net energy savings. The pricing scheme,in conjunction with a two dimensional bid structure, incentivizes cooperation at the terminal nodes for better interference management at receivers and for cooperative relaying. It is shown that, for the proposed auctioning strategy, network operators are guaranteed revenue gains, mobile nodes’ dominant strategy is to bid their true valuation of their energy resources, and overall effective energy gains occur under the assumption of a reserve price for bidding. Simulation results show that significant energy savings can be achieved by employing this auctioning mechanism for a 3G cellular set-up.

        • KCI등재

          Prediction of Patient Management in COVID-19 Using Deep Learning-Based Fully Automated Extraction of Cardiothoracic CT Metrics and Laboratory Findings

          Weikert Thomas,Rapaka Saikiran,Grbic Sasa,Re Thomas,Chaganti Shikha,Winkel David J.,Anastasopoulos Constantin,Niemann Tilo,Wiggli Benedikt J.,Bremerich Jens,Twerenbold Raphael,Sommer Gregor,Comaniciu 대한영상의학회 2021 Korean Journal of Radiology Vol.22 No.6

          Objective: To extract pulmonary and cardiovascular metrics from chest CTs of patients with coronavirus disease 2019 (COVID-19) using a fully automated deep learning-based approach and assess their potential to predict patient management. Materials and Methods: All initial chest CTs of patients who tested positive for severe acute respiratory syndrome coronavirus 2 at our emergency department between March 25 and April 25, 2020, were identified (n = 120). Three patient management groups were defined: group 1 (outpatient), group 2 (general ward), and group 3 (intensive care unit [ICU]). Multiple pulmonary and cardiovascular metrics were extracted from the chest CT images using deep learning. Additionally, six laboratory findings indicating inflammation and cellular damage were considered. Differences in CT metrics, laboratory findings, and demographics between the patient management groups were assessed. The potential of these parameters to predict patients’ needs for intensive care (yes/no) was analyzed using logistic regression and receiver operating characteristic curves. Internal and external validity were assessed using 109 independent chest CT scans. Results: While demographic parameters alone (sex and age) were not sufficient to predict ICU management status, both CT metrics alone (including both pulmonary and cardiovascular metrics; area under the curve [AUC] = 0.88; 95% confidence interval [CI] = 0.79–0.97) and laboratory findings alone (C-reactive protein, lactate dehydrogenase, white blood cell count, and albumin; AUC = 0.86; 95% CI = 0.77–0.94) were good classifiers. Excellent performance was achieved by a combination of demographic parameters, CT metrics, and laboratory findings (AUC = 0.91; 95% CI = 0.85–0.98). Application of a model that combined both pulmonary CT metrics and demographic parameters on a dataset from another hospital indicated its external validity (AUC = 0.77; 95% CI = 0.66–0.88). Conclusion: Chest CT of patients with COVID-19 contains valuable information that can be accessed using automated image analysis. These metrics are useful for the prediction of patient management.

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