Analytical framework for optimized feature extraction for upgrading occupancy sensing performance

The adoption of the occupancy sensors has become an inevitable in commercial and non-commercial security devices, owing to their proficiency in the energy management. It has been found that the usages of conventional sensors is shrouded with operational problems, hence the use of the Doppler radar offers better mitigation of such problems. However, the usage of Doppler radar towards occupancy sensing in existing system is found to be very much in infancy stage. Moreover, the performance of monitoring using Doppler radar is yet to be improved more. Therefore, this paper introduces a simplified framework for enriching the event sensing performance by efficient selection of minimal robust attributes using Doppler radar. Adoption of analytical methodology has been carried out to find that different machine learning approaches could be further used for improving the accuracy performance for the feature that has been extracted in the proposed system of occuancy system

Spectrum occupancy reconstruction in distributed cognitive radio networks using deep learning

Spectrum occupancy reconstruction is an important issue often encountered in collaborative spectrum sensing in distributed cognitive radio networks (CRNs). This issue arises when the spectrum sensing data that are collaborated by secondary users have gaps of missing entries. Many data imputation techniques, such as matrix completion techniques, have shown great promise in dealing with missing spectrum sensing observations by reconstructing the spectrum occupancy data matrix. However, matrix completion approaches achieve lower reconstruction resolution due to the use of standard singular value decomposition (SVD), which is designed for more general matrices. In this paper, we consider the problem of spectrum occupancy reconstruction where the spectrum sensing results across the CRN are represented as a plenary grid on a Markov random eld. We formulate the problem as a magnetic excitation state recovery problem, and the stochastic gradient descent (SGD) method is applied to solve the matrix factorization. SGD is able to learn and impute the missing values with a low reconstruction error compared with SVD. The graphical and numerical results show that the SGD algorithm competes favorably SVD in the matrix factorization by taking advantage of correlations in multiple dimensions.The Association of Commonwealth Universities under Grant FE-2015-26, and in part by the Sentech Chair in Broadband Wireless Multimedia Communications.http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6287639am2020Electrical, Electronic and Computer Engineerin