جستجوی مقالات مرتبط با کلیدواژه « computational complexity » در نشریات گروه « برق »

The efficiency of human action recognition systems depends on extracting appropriate representations from the video data. In recent years, deep learning methods have been proposed to extract efficient spatial-temporal representations. Deep learning methods, on the other hand, have a high computational complexity for development over temporal domain. Challenges such as the sparsity and limitation of discriminative data, and highly noise factors increase the computational complexity of representing human actions. Therefore, creating a high accurate representation requires a very high computational cost. In this paper, spatial and temporal deep learning networks have been enhanced by adding appropriate feature selection mechanisms to reduce the search space. In this regard, non-online and online feature selection mechanisms have been studied to identify human actions with less computational complexity and higher accuracy. The results showed that the non-linear feature selection mechanism leads to a significant reduction in computational complexity and the online feature selection mechanism increases the accuracy while controlling the computational complexity.

The efficiency of human action recognition systems depends on extracting appropriate representations from the video data. In recent years, deep learning methods have been proposed to extract efficient spatial-temporal representations. Deep learning methods, on the other hand, have a high computational complexity for development over temporal domain. Challenges such as the sparsity and limitation of discriminative data, and highly noise factors increase the computational complexity of representing human actions. Therefore, creating a high accurate representation requires a very high computational cost. In this paper, spatial and temporal deep learning networks have been enhanced by adding appropriate feature selection mechanisms to reduce the search space. In this regard, non-online and online feature selection mechanisms have been studied to identify human actions with less computational complexity and higher accuracy. The results showed that the non-linear feature selection mechanism leads to a significant reduction in computational complexity and the online feature selection mechanism increases the accuracy while controlling the computational complexity.

In recent years, Massive Multiple-Input Multiple-Output (MMIMO) systems have emerged as a promising enabling technology to address the ever-growing demand of data traffic in the next generation of wireless networks. These systems received a great deal of interest due to their promise of high performance gains. These gains are mainly achieved at the expense of having a tremendous number of antenna elements within a relatively small physical platform. The growth in the number of antennas, however, significantly increases the Radio Frequency (RF) expenses. Therefore, developing solutions to alleviate this problem has become a major topic of interest. In this paper a stepwise regression approach is proposed to find an effective subset of transmit antennas. In each step of our scheme, we choose the antenna that maximizes the achievable rate and discard the antenna that has been selected in the previous step.  Our numerical results show that the proposed antenna selection scheme outperforms the other schemes in the literature.