Vector orthogonal frequency division multiplexing (V-OFDM) for single transmit antenna systems is a generalization of OFDM where single-carrier frequency domain equalization (SC-FDE) and OFDM are just two special case...
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Vector orthogonal frequency division multiplexing (V-OFDM) for single transmit antenna systems is a generalization of OFDM where single-carrier frequency domain equalization (SC-FDE) and OFDM are just two special cases. Phase noise in a V-OFDM system leads to a common vector block phase error (CVBPE) as well as an inter vector block carrier interference (IVBCI) effect. Severe performance degradation results if these two effects are not estimated and compensated for. In this paper blind and semi-blind phase noise estimation and compensation in a V-OFDM system is investigated by using the expectation maximization (EM) algorithm. This is motivated by the fact that conventional frequency domain phase noise suppression schemes based on pilot-aided CVBPE estimation and compensation are not spectrally efficient as the vector block size is increased. Two novel algorithms are proposed, one estimates the CVBPE only and the other estimates the entire phase noise sequence in the time-domain. Simulations show that with the proposed algorithms V-OFDM can outperform OFDM system with conventional data aided or soft information aided phase noise CPE correction.
As an important category of social communication,facial expressions provide abundant social and emotional ***,it has been a challenge for computer systems to provide real time accurate recognition of facial expression...
As an important category of social communication,facial expressions provide abundant social and emotional ***,it has been a challenge for computer systems to provide real time accurate recognition of facial expressions from multimedia content and make the analysis *** address these issues,we propose a sparse tagging-like approach to jointly learn Action Units for facial expressions recognition,which can be utilized for social interaction ***,we regard the recognition of the combination of Action Units as tagging *** this approach,the computational complexity is substantially reduced because only matrix multiplications are *** order to make the analysis interpretable,we introduce a sparse term into our approach to reinforce the sparseness of the combination of Action *** in four benchmark datasets demonstrate that,compared with existing algorithms,our method achieves much faster speed,and higher interpretability and robustness,while yielding a matching accuracy.
Phylogenetic trees represent the historical evolutionary relationships between different species or organisms. Creating and maintaining a repository of phylogenetic trees is one of the major objectives of molecular ev...
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作者:
Yanrong ShiSchool of Computer Science and Technology
Shandong Institute of Business and TechnologyYantaiChina Key Laboratory of Intelligent Information Processing in Universities of ShandongShandong Institute of Business and Technology
The computer experiment teaching is a necessary step to verify the theory of classroom teaching,is an important way to cultivate students' practical ability,innovation ability and team spirit of *** at the existin...
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The computer experiment teaching is a necessary step to verify the theory of classroom teaching,is an important way to cultivate students' practical ability,innovation ability and team spirit of *** at the existing problems in the computer experiment teaching,we carried out to study and practice the teaching reform of computer experiments from several aspects,and achieved certain results.
Crowd evacuation simulation is of great significance for strategy design in emergency, resulting in serious injuries and casualties. A majority of existing evacuation models overlooked the interactions and influences ...
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This paper proposes a boundary feedback control design for open canal networks using the linearization of boundary conditions. For open canal networks with any types of cross-sections, which can be modelled by the Sai...
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In recent years, multi-label learning is becoming a new research hot spot. In this paper, we proposed a multi-label learning method called LTFML based on the idea of trying to determine the typical features for each c...
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The use of cloud storage services has increased rapidly in many organizations. However, the security of data stored in the cloud still remains major concerns. Due to risks of service availability failure and the possi...
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The use of cloud storage services has increased rapidly in many organizations. However, the security of data stored in the cloud still remains major concerns. Due to risks of service availability failure and the possibility of malicious insiders in the single cloud, researches about "multi-clouds" have emerged recently. Recent researches that are related to multi-clouds have some limitations. For instance, Redundant Array of Cloud Storage (RACS) does not ensure data confidentiality. The performance of system is low in DEPSKY and so on. This paper presents a data hiding scheme that is based on Lagrange interpolation algorithm and multi-clouds. This scheme addresses data availability, data confidentiality and vendor lock-in issue in cloud storage services through the erasure codes and Lagrange interpolation algorithm instead of encryption to store data on diverse clouds. The performance analysis was done through experiences. We observed that our scheme improved the performance for the response times of data uploaded and downloaded.
As an important reflection of human cognitive ability, the multi-granulation analysis gets more reasonable solution of a problem in comparison to the single granulation. Clustering analysis is an active area of machin...
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ISBN:
(纸本)9781467372220
As an important reflection of human cognitive ability, the multi-granulation analysis gets more reasonable solution of a problem in comparison to the single granulation. Clustering analysis is an active area of machine learning and a fundamental technique of information granulation. By using different clustering algorithms and different parameters of an algorithm, a data set can be granulated into multiple granular spaces. Clustering ensemble with these granular spaces is an effective strategy of multigranulation information fusion. The existing algorithms of clustering ensemble can be categorized into three types: feature-based method, combinatorial method and graph-based method. Given the fact that every type of methods has their own advantages and disadvantages, combining their advantages will obtain better granulation results. Based on this consideration, this paper introduces a Dempster-Shafer evidence theory based clustering ensemble method that combines advantages of combinatorial method and graph-based method. In this strategy, the definition of mass functions considers neighbors of an object using the graph binarization and the final clustering ensemble result is generated by applying the Dempster's combination rule. The form of the Dempster's combination rule makes the algorithm conforming to the pattern of combinatorial method. Experimental results show that the proposed method yields better performance in comparison with other seven clustering ensemble methods conducted on fourteen numerical real-world data sets from the UCI Machine Learning Repository.
This paper presents a high-quality very large scale integration (VLSI) global router in X-architecture, called XGRouter, that heavily relies on integer linear programming (ILP) techniques, partition strategy and parti...
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