| 1. |
- Alvarez, Lluc, et al.
(författare)
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eProcessor: European, Extendable, Energy-Efficient, Extreme-Scale, Extensible, Processor Ecosystem
- 2023
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Ingår i: Proceedings of the 20th ACM International Conference on Computing Frontiers 2023, CF 2023. ; , s. 309-314
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Konferensbidrag (refereegranskat)abstract
- The eProcessor project aims at creating a RISC-V full stack ecosystem. The eProcessor architecture combines a high-performance out-of-order core with energy-efficient accelerators for vector processing and artificial intelligence with reduced-precision functional units. The design of this architecture follows a hardware/software co-design approach with relevant application use cases from the high-performance computing, bioinformatics and artificial intelligence domains. Two eProcessor prototypes will be developed based on two fabricated eProcessor ASICs integrated into a computer-on-module.
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| 2. |
- Shokri-Ghadikolaei, Hossein, 1987-, et al.
(författare)
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Energy efficient spectrum sensing and handoff strategies in cognitive radio networks
- 2013
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Ingår i: IEEE Communications Magazine. - : IEEE Communications Society. - 0163-6804 .- 1558-1896.
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Tidskriftsartikel (refereegranskat)abstract
- The limited spectrum resources and dramatic growth of high data rate communications have motivated opportunistic spectrum access using the promising concept of cognitive radio networks. Although this concept has emerged primarily to enhance spectrum utilization, the importance of energy consumption poses new challenges, because energy efficiency and communication performance can be at odds. In this paper, the existing approaches to energy efficiency spectrum sensing and handoff are classified. The tradeoff between energy consumption and throughput is established as function of the numerous design parameters of cognitive radio networks, both in the case of local and of cooperative spectrum sensing. It is argued that a number of important aspects still needs to be researched, such as fairness, dynamic behavior, reactive and proactive schemes for energy efficiency.
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| 3. |
- Van Holsbeke, Caroline, et al.
(författare)
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External validation of mathematical models to distinguish between benign and malignant adnexal tumors: A multicenter study by the International Ovarian Tumor Analysis group
- 2007
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Ingår i: Clinical Cancer Research. - 1078-0432. ; 13:15, s. 4440-4447
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Tidskriftsartikel (refereegranskat)abstract
- Purpose: Several scoring systems have been developed to distinguish between benign and malignant adnexal tumors. However, few of them have been externally validated in new populations. Our aim was to compare their performance on a prospectively collected large multicenter data set. Experimental Design: In phase I of the International Ovarian Tumor Analysis multicenter study, patients with a persistent adnexal mass were examined with transvaginal ultrasound and color Doppler imaging. More than 50 end point variables were prospectively recorded for analysis. The outcome measure was the histologic classification of excised tissue as malignant or benign. We used the International Ovarian Tumor Analysis data to test the accuracy of previously published scoring systems. Receiver operating characteristic curves were constructed to compare the performance of the models. Results: Data from 1,066 patients were included; 800 patients (75%) had benign tumors and 266 patients (25%) had malignant tumors. The morphologic scoring system used by Lerner gave an area under the receiver operating characteristic curve (AUC) of 0.68, whereas the multimodal risk of malignancy index used by Jacobs gave an AUC of 0.88. The corresponding values for logistic regression and artificial neural network models varied between 0.76 and 0.91 and between 0.87 and 0.90, respectively. Advanced kernel-based classifiers gave an AUC of up to 0.92. Conclusion: The performance of the risk of malignancy index was similar to that of most logistic regression and artificial neural network models. The best result was obtained with a relevance vector machine with radial basis function kernel. Because the models were tested on a large multicenter data set, results are likely to be generally applicable.
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