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    Two-dimensional multi-parameter adaptation of noise, linearity, and power consumption in wireless receivers

    , Article IEEE Transactions on Circuits and Systems I: Regular Papers ; Vol. 61, issue. 8 , July , 2014 , p. 2433-2443 Meghdadi, M ; Sharif Bakhtiar, M ; Sharif University of Technology
    Abstract
    This paper presents a general method for real-time adaptation of wireless receivers according to the prevailing reception conditions. In order to maintain the desired signal quality at the minimum possible power dissipation, the method performs an optimal trade-off between noise, linearity, and power consumption in the building blocks of the receiver. This is achieved by continuously monitoring the signal-to-noise plus interference ratio (SNIR) and accordingly tuning the adaptation parameters embedded in the receiver design. A prototype DVB-H receiver chip, implemented in a standard 0.18-μ m CMOS process, is used as the test vehicle. By properly trading noise with linearity in the receiver,... 

    A rapid neural network-based demand estimation for generic buildings considering the effect of soft/weak story

    , Article Structure and Infrastructure Engineering ; 2022 ; 15732479 (ISSN) Salkhordeh, M ; Alishahiha, F ; Mirtaheri, M ; Soroushian, S ; Sharif University of Technology
    Taylor and Francis Ltd  2022
    Abstract
    Recent earthquakes clarified that existing a soft/weak-story in a building could completely invert the failure mechanisms of the structure. Several studies were implemented to evaluate the potential risk subjected to the buildings under the earthquake hazard. However, these researchers discarded the effect of soft/weak-story on the demand parameters of their models. This paper presents a rapid demand estimation framework for generic buildings considering the effect of soft/weak-story. In this regard, the simplified model developed according to the HAZUS approach is rectified to apply the effect of soft/weak-story on the structural behavior of the generic buildings. Artificial neural networks...