| Grant number: | 14/23936-4 |
| Support Opportunities: | Scholarships in Brazil - Post-Doctoral |
| Start date: | April 01, 2015 |
| End date: | May 31, 2019 |
| Field of knowledge: | Engineering - Electrical Engineering - Telecommunications |
| Agreement: | Coordination of Improvement of Higher Education Personnel (CAPES) |
| Principal Investigator: | João Marcos Travassos Romano |
| Grantee: | Michele Nazareth da Costa |
| Host Institution: | Faculdade de Engenharia Elétrica e de Computação (FEEC). Universidade Estadual de Campinas (UNICAMP). Campinas , SP, Brazil |
| Associated scholarship(s): | 16/14142-0 - Tensor networks and deep learning for large scale machine learning and signal processing problems, BE.EP.PD |
Abstract Several recent research topics related to the information processing are characterized by an increasing amount of data. As a consequence of this increase, the processing of these data tends to require more sophisticated methods than those classically employed in matrix approaches. Thus, the tensor approach is becoming a natural and powerful tool in this context, as it ensures that all data and intrinsic interactions are taking into account in the processing. Tensor methods can be developed and proposed from extensions of classical methods of matrix factorization and also from well-known tensor models, such as PARAFAC/CANDECOMP and Tucker.In her thesis, the candidate proposed a transmission system based on a Tensor Space-Time (TST) coding, with resources of antenna allocation, for MIMO wireless communication systems. The strategy adopted to improve the performance consisted of the joint use of different diversities inherent to the received signals. A new tensor decomposition was proposed by the candidate from which the signals received by multiple antennas were represented. Despite the promising results presented in her thesis, there are some aspects to be further investigated regarding the study of optimal allocation structure, optimization of the receivers, and analysis of the multiuser case. These issues compose the first part of this project, aimed at the application in Telecommunications. In contrast, it is also noticed that several digital signal processing techniques which are typically applied in Telecommunications context have been increasingly employed in the processing of seismic signals. Thus, the second part of this project is related to this application, in which the idea is to explore the tensorial formalism. Therefore, different topics of research will be investigated based on Principal Component Analysis (PCA) for separation of seismic waves, noise attenuation, and reconstruction of corrupted or incomplete data. The tensor tools will be employed for modeling and processing of seismic multidimensional data, in order to provide a better use of the information underlying seismic acquisitions, and also to aid the image generation of the subsurface volume of the region to be explored. (AU) | |
| News published in Agência FAPESP Newsletter about the scholarship: | |
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