| 1. |
- Huang, Yixuan, et al.
(författare)
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Designing Low-PAPR Waveform for OFDM-Based RadCom Systems
- 2022
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Ingår i: IEEE Transactions on Wireless Communications. - : Institute of Electrical and Electronics Engineers (IEEE). - 1536-1276 .- 1558-2248. ; 21:9, s. 6979-6993
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Tidskriftsartikel (refereegranskat)abstract
- This paper is focused on the fusion of radar and wireless communication, called RadCom, which has been extensively studied in recent years for future intelligent transportation systems. We propose a new waveform design algorithm for reducing peak-to-average power ratio (PAPR) in OFDM-based RadCom systems. We consider a flexible and generic RadCom structure in which a number of non-contiguous sub-bands for data transmission are located within a large contiguous spectrum hand for radar detection/sensing. New RadCom waveforms with low PAPR are obtained by carrying out optimization over those subcarriers which are complementary to the communication bands. As an application of the majorization-minimization (MM) optimization method, our major contribution is an l-norm cyclic algorithm which is capable of efficiently reducing the maximum PAPR of RadCom waveforms. We show by numerical simulation results that significant performance enhancements can be achieved compared to OFDM RadCom waveforms from legacy approaches.
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| 2. |
- Moretti, Rocco, et al.
(författare)
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Community-wide evaluation of methods for predicting the effect of mutations on protein-protein interactions
- 2013
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Ingår i: Proteins. - : Wiley. - 0887-3585 .- 1097-0134. ; 81:11, s. 1980-1987
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Tidskriftsartikel (refereegranskat)abstract
- Community-wide blind prediction experiments such as CAPRI and CASP provide an objective measure of the current state of predictive methodology. Here we describe a community-wide assessment of methods to predict the effects of mutations on protein-protein interactions. Twenty-two groups predicted the effects of comprehensive saturation mutagenesis for two designed influenza hemagglutinin binders and the results were compared with experimental yeast display enrichment data obtained using deep sequencing. The most successful methods explicitly considered the effects of mutation on monomer stability in addition to binding affinity, carried out explicit side-chain sampling and backbone relaxation, evaluated packing, electrostatic, and solvation effects, and correctly identified around a third of the beneficial mutations. Much room for improvement remains for even the best techniques, and large-scale fitness landscapes should continue to provide an excellent test bed for continued evaluation of both existing and new prediction methodologies. Proteins 2013; 81:1980-1987.
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| 3. |
- Wan, Jiafu, et al.
(författare)
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A Manufacturing Big Data Solution for Active Preventive Maintenance
- 2017
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Ingår i: IEEE Transactions on Industrial Informatics. - : Institute of Electrical and Electronics Engineers (IEEE). - 1551-3203 .- 1941-0050. ; 13:4, s. 2039-2047
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Tidskriftsartikel (refereegranskat)abstract
- Industry 4.0 has become more popular due to recent developments in Cyber-Physical Systems (CPS), big data, cloud computing, and industrial wireless networks. Intelligent manufacturing has produced a revolutionary change, and evolving applications such as product lifecycle management are becoming a reality. In this paper, we propose and implement a manufacturing big data solution for active preventive maintenance in manufacturing environments. First, we provide the system architecture that is used for active preventive maintenance. Then, we analyze the method used for collection of manufacturing big data according to the data characteristics. Subsequently, we perform data processing in the cloud, including the cloud layer architecture, the real-time active maintenance mechanism, and the off-line prediction and analysis method. Finally, we analyze a prototype platform and implement experiments to compare the traditionally-used method with the proposed active preventive maintenance method. The manufacturing big data method used for active preventive maintenance has the potential to accelerate implementation of Industry 4.0.
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