MIMO-radar has better parametric identifiability but compared to phased-array radar it shows loss in signal-tonoise ratio due to non-coherent processing. To exploit the benefits of both MIMO-radar and phased-array two transmit covariance matrices are found. Both of the covariance matrices yield gain in signal-to-interference-plus-noise ratio (SINR) compared to MIMO-radar and have lower side-lobe levels (SLL)'s compared to phased-array and MIMOradar. Moreover, in contrast to recently introduced phased-MIMO scheme, where each antenna transmit different power, our proposed schemes allows same power transmission from each antenna. The SLL's of the proposed first covariance matrix are higher than the phased-MIMO scheme while the SLL's of the second proposed covariance matrix are lower than the phased-MIMO scheme. The first covariance matrix is generated using an auto-regressive process, which allow us to change the SINR and side lobe levels by changing the auto-regressive parameter, while to generate the second covariance matrix the values of sine function between 0 and π with the step size of π/nT are used to form a positive-semidefinite Toeplitiz matrix, where nT is the number of transmit antennas. Simulation results validate our analytical results. Index Terms MIMO radar, phased MIMO, colocated antennas. I. INTRODUCTION Recently several researchers have considered the application of multiple-input multiple-output (MIMO) techniques developed for wireless communication systems to the radar systems [1]-[3]. In MIMO communication systems, n T antennas are deployed at the transmitter and n R antennas at the receiver to increase the data rate and provide
Motivation
Extracting useful feature set which contains significant discriminatory information is a critical step in effectively presenting sequence data to predict structural, functional, interaction and expression of proteins, DNAs and RNAs. Also, being able to filter features with significant information and avoid sparsity in the extracted features require the employment of efficient feature selection techniques. Here we present PyFeat as a practical and easy to use toolkit implemented in Python for extracting various features from proteins, DNAs and RNAs. To build PyFeat we mainly focused on extracting features that capture information about the interaction of neighboring residues to be able to provide more local information. We then employ AdaBoost technique to select features with maximum discriminatory information. In this way, we can significantly reduce the number of extracted features and enable PyFeat to represent the combination of effective features from large neighboring residues. As a result, PyFeat is able to extract features from 13 different techniques and represent context free combination of effective features. The source code for PyFeat standalone toolkit and employed benchmarks with a comprehensive user manual explaining its system and workflow in a step by step manner are publicly available.
Results
https://github.com/mrzResearchArena/PyFeat/blob/master/RESULTS.md.
Availability and implementation
Toolkit, source code and manual to use PyFeat: https://github.com/mrzResearchArena/PyFeat/
Supplementary information
Supplementary data are available at Bioinformatics online.
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