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- 저자 : V. K. Hanumantha Rao Talari (검색결과 3 건)
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Rambabu Kusuma,V. K. Hanumantha Rao Talari 한국전기전자재료학회 2023 Transactions on Electrical and Electronic Material Vol.24 No.3
Fully Depleted Silicon On Insulator (FDSOI) structures are present-era technology as it has enhanced control over Short Channel Effects in the sub-nanometre regime. This paper studies the analog and radio frequency performance of junctionless FinFET with dual material gate (DMG JLFinFET) based on FDSOI for low power applications. We extracted analog and radio frequency parameters with the variation of fin height (FH = 10 nm to 30 nm) and fin width (FW = 46 nm). The parameters like transconductance (gm), transconductance generation factor, cut-off frequency (fT), intrinsic delay (τ), gate capacitance (Cgg), gate to source capacitance (Cgs), gate to drain capacitance (Cgd), and transconductance frequency product, gain bandwidth product are calculated. At FH = 6 nm all the parameters are increased except time delay which was small decrement. Similarly for FW also all the parameters are improved with increment of fin width except time delay. In contempt Cgd and Cgg are less impact on dimensional variation. From this study it reveals that, in FinFET design, designers have to consider dimensional variations in Anlog/RF parameters and FinFET is suitable candidate for nano scale low power Anlog/RF applications. In this work all these simulations are carried out by Cogenda Visual TCAD.
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Neural source localization using particle filter with optimal proportional set resampling
Veeramalla, Santhosh Kumar,Talari, V.K. Hanumantha Rao Electronics and Telecommunications Research Instit 2020 ETRI Journal Vol.42 No.6
To recover the neural activity from Magnetoencephalography (MEG) and Electroencephalography (EEG) measurements, we need to solve the inverse problem by utilizing the relation between dipole sources and the data generated by dipolar sources. In this study, we propose a new approach based on the implementation of a particle filter (PF) that uses minimum sampling variance resampling methodology to track the neural dipole sources of cerebral activity. We use this approach for the EEG data and demonstrate that it can naturally estimate the sources more precisely than the traditional systematic resampling scheme in PFs.
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Santhosh Kumar Veeramalla,V. K. Hanumantha Rao Talari 대한의용생체공학회 2020 Biomedical Engineering Letters (BMEL) Vol.10 No.2
Tracking and detection of neural activity has numerous applications in the medical research fi eld. By considering neuralsources, it can be monitored by electroencephalography (EEG). In this paper, we focus primarily on developing advancedsignal processing methods for locating neural sources. Due to its high performance in state estimation and tracking, particlefi lter was used to locate neural sources. However, particle degeneracy limits the performance of particle fi lters in the mostutmost situations. A few resampling methods were subsequently proposed to ease this issue. These resampling methods,however, take on heavy computational costs. In this article, we aim to investigate the Partial Stratifi ed Resampling algorithmwhich is time-effi cient that can be used to locate neural sources and compare them to conventional resampling algorithms. This work is aimed at refl ecting on the capabilities of various resampling algorithms and estimating the performance oflocating neural sources. Simulated data and real EEG data are used to conduct evaluation and comparison experiments.
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