This paper proposes four-parallel pipelined fast Fourier transform (FFT) architecture for the discrete Fourier transform (DFT) computation of quadrature-phase-shiftkeying Orthogonal Frequency Division Multiplexing (QPSK-OFDM) signals. The first stage of the architecture is optimized with the multiplexer blocks that contain output values of QPSK as inputs. Furthermore, the proposed architecture employs a complex constant multipliers (CCMs) using adders and shifters that can reduce the total hardware of the design. The proposed 64-point pipeline architecture is modeled and implemented using UMC 65 nm CMOS technology with a supply voltage of 1.2 V. The results demonstrate that the proposed FFT architecture significantly reduces the hardware cost and power consumption in comparison to the recent FFT architecture.

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Background: Now a days the whole World is submerged with digital electronics devices round the clock for all utilities. Even the doctor community also is not spared by these digital gadgets. Almost every doctor in India is permanently having and operating a smart phone and laptops or desktops plus vide computer applications.Methods: This cross-sectional study was conducted among 45 doctors across 7 departments of Government Medical College, Vellore. Mean, median, mode, standard deviation was used for quantitative data and Pearson chi square test and logistic regression was used for qualitative data using trial version of SPSS 22.Results: The mean score was 11.2±2.8 with mean scoring percentile of 38.8±6.5. There was Pearsons Chi square significance for Variables like exposure to external resources and, Books related to cyber security, age less than 34 years and average spending time with electronic devices >2.5 hours per day, designation MD versus MBBS, gender difference.Conclusions: On running logistic regression the multivariate analysis for study variables with statistical significance was seen for four variables- exposure to external resources, and books related to cyber security, age <34 years, average spending time with electronic devices >2.5 hours per day. Designation and gender variables lost their significance on logistic regression analysis.