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Hybrid adaptive beamforming approach for antenna array fed parabolic reflector for C-band applications

Bawane Sheetal, Kumar Panda Debendra

Journal of Automation Mobile Robotics and Intelligent Systems

2023

Vol. 17, No. 1

45--50

This paper presents the design of a parabolic reflector fed through a patch antenna array feed to enhance its directivity and radiation properties. Adaptive beam for‐ mers steer and alter an array’s beam pattern to increase signal reception and minimize interference. Weight selec‐ tion is a critical difficulty in achieving low SLL and beam width. Low Side Lobe Level [SLL]and narrow beam reduce antenna radiation and reception. Adjusting the weights reduces SLL and tilts the nulls. Adaptive beam formers are successful signal processors if their array output con‐ verges to the required signal. Smart antenna weights can be determined using any window function. Half Power Beam Width and SLL could be used to explore different algorithms. Both must be low for excellent smart antenna performance. In noisy settings, ACLMS and CLMS create narrow beams and side lobes. AANGD offers more control than CLMS and ACLMS. The blend of CLMS and ACLMS is more effective at signal convergence than CLMS and AANGD. It presents an alternative to the conventionally used horn‐based feed network for C‐band applications such as satellite communication. Broadside radiation patterns and 4x4 circular patch antenna arrays are used in the proposed design. 1400 aperture illumination is pro‐ vided by the array’s feed parabolic reflector, whose F/D ratio is 0.36. The proposed design’s efficacy is assessed using simulation analysis.

Fuzzy Logic Based Intelligent Data Sensitive Security Model for Big Data in Healthcare

Dubey Somya, Verma Dhanraj

International Journal of Electronics and Telecommunications

2022

Vol. 68, No. 2

245--250

An intelligent security model for the big data environment is presented in this paper. The proposed security framework is data sensitive in nature and the level of security offered is defined on the basis of the data secrecy standard. The application area preferred in this work is the healthcare sector where the amount of data generated through the digitization and aggregation of medical equipment’s readings and reports is huge. The handling and processing of this great amount of data has posed a serious challenge to the researchers. The analytical outcomes of the study of this data are further used for the advancement of the medical prognostics and diagnostics. Security and privacy of this data is also a very important aspect in healthcare sector and has been incorporated in the healthcare act of many countries. However, the security level implemented conventionally is of same level to the complete data which not a smart strategy considering the varying level of sensitivity of data. It is inefficient for the data of high sensitivity and redundant for the data of low sensitivity. An intelligent data sensitive security framework is therefore proposed in this paper which provides the security level best suited for the data of given sensitivity. Fuzzy logic decision making technique is used in this work to determine the security level for a respective sensitivity level. Various patient attributes are used to take the intelligent decision about the security level through fuzzy inference system. The effectiveness and the efficacy of the proposed work is verified through the experimental study.