SummaryStatic random access memory (SRAM)‐based cache memory is an essential part of electronic devices. As the technology node reduces, the power loss and stability has become the major problems. Several SRAM cells had been developed to address the stability and power loss problem. But still, it is a challenge to achieve balance performance among all the parameters of the SRAM cell for sub‐nanometer technology. This paper proposes a novel SRAM cell, which is having comparatively less total, static power loss, less delay, and high stability compared with the conventional cells for 45‐nm complementary metal‐oxide‐semiconductor (CMOS) technology. The total power cost of the proposed 10T cell has been reduced by 90.3%, 85.84%, 51.02%, and 90.9% compared with 6T, N‐controlled (NC), 10T sub, and 10T, respectively. Similarly, the static power cost of the proposed cell has been reduced by 55.17%, 5.72%, ‐41.6%, and 52.9% compared with 6T, NC, 10T‐sub, and 10T, respectively. The proposed cell provides better stability, less delay, and comparable area compared with other considered 10T cells. Finally, the Monte Carlo (MC) simulation and process analysis of SRAM cells validate the efficiency of the proposed 10T cell.
In this paper, the problem of direction of arrival estimation is addressed by employing Bayesian learning technique in sparse domain. This paper deals with the inference of sparse Bayesian learning (SBL) for both single measurement vector (SMV) and multiple measurement vector (MMV) and its applicability to estimate the arriving signal’s direction at the receiving antenna array; particularly considered to be a uniform linear array. We also derive the hyperparameter updating equations by maximizing the posterior of hyperparameters and exhibit the results for nonzero hyperprior scalars. The results presented in this paper, shows that the resolution and speed of the proposed algorithm is comparatively improved with almost zero failure rate and minimum mean square error of signal’s direction estimate.
In South Asia, the fat female body is accorded narrative prominence to construct different synonyms of beauty and power. It is frequently consigned to the edges of fictional realms in visual representations and popular cultures. The standards of beauty (i.e., slender body, seductive dress, skin colour, make-up, etc.) are colonising the brains of women by dividing them and forcing them to adhere to the beauty norms by limiting their performance to the erotic subject. The article analyses the film Fanney Khan (2018) as an imperative study to understand how the concept of “fat body with ability” is subjugated to the conventional idea of “fat = inabilities, unhygienic, unhealthy, diseased and disabled” by the hegemonic society through the protagonist and eventually dismantling the same. This article investigates how juvenile fat subjects parley through numerous discursive interactions in the film Fanney Khan (2018). It illustrates how the female lead in the film confronts, resists and ultimately debilitates the conventional notions of the fat body and beauty standards. Although fatness is represented in this film as either a source of extreme animosity and conflict or a matter of desexualised femininity and conventional clothing choice, it nonetheless serves as a counter-hegemonic ideal that destabilises fatphobia (Singh 2021).
Electronic system building has become highly competitive from the point of reducing area, power and increasing the speed. To address these issues, many technologies are being tried. Quantum Dot Cellular Automation is one such technology. This technology is in its infant state as far as its physical implementation and verification are concerned. However the researchers have come out with theoretical models and proposed many compact, fast and low power dissipating digital blocks. Decoders are one of the standard combinational modules used as the basic building blocks for efficient digital system design. Here in this paper we implement decoders in QCA using different techniques and analyze them with respect to area, time and energy. From the implementation and simulation results obtained using QCADESIGNER version 2.0.3 we have observed that, the 2:4 decoders implemented with and without the enable input using both single layer and multilayer techniques utilize minimum area, time and energy.
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