scholarly journals Chebyshev Filter Diagonalization on Modern Manycore Processors and GPGPUs

2018 ◽  
pp. 329-349 ◽  
Author(s):  
Moritz Kreutzer ◽  
Dominik Ernst ◽  
Alan R. Bishop ◽  
Holger Fehske ◽  
Georg Hager ◽  
...  
Keyword(s):  
Filter Diagonalization ◽  
Manycore Processors ◽  
Chebyshev Filter

scholarly journals High-performance implementation of Chebyshev filter diagonalization for interior eigenvalue computations

2016 ◽  
Vol 325 ◽  
pp. 226-243 ◽  
Author(s):  
Andreas Pieper ◽  
Moritz Kreutzer ◽  
Andreas Alvermann ◽  
Martin Galgon ◽  
Holger Fehske ◽  
...  
Keyword(s):  
High Performance ◽  
Filter Diagonalization ◽  
Chebyshev Filter

scholarly journals A 0.18um CMOS Low Noise Amplifier for 3-5ghz UWB Receivers

2018 ◽  
Vol 7 (3.6) ◽  
pp. 84
Author(s):  
N Malika Begum ◽  
W Yasmeen
Keyword(s):  
Power Supply ◽  
Noise Figure ◽  
Cmos Technology ◽  
Low Noise ◽  
Low Noise Amplifier ◽  
Ultra Wideband ◽  
Noise Amplifier ◽  
5 Ghz ◽  
Chebyshev Filter ◽  
Minimum Noise

This paper presents an Ultra-Wideband (UWB) 3-5 GHz Low Noise Amplifier (LNA) employing Chebyshev filter. The LNA has been designed using Cadence 0.18um CMOS technology. Proposed LNA achieves a minimum noise figure of 2.2dB, power gain of 9dB.The power consumption is 6.3mW from 1.8V power supply.  

Fast and accurate thermal modeling and simulation of manycore processors and workloads

2013 ◽  
Vol 44 (11) ◽  
pp. 986-993 ◽  
Author(s):  
Bartosz Wojciechowski ◽  
Krzysztof S. Berezowski ◽  
Piotr Patronik ◽  
Janusz Biernat
Keyword(s):  
Modeling And Simulation ◽  
Thermal Modeling ◽  
Manycore Processors

ON HARMONIC INVERSION OF CROSS-CORRELATION FUNCTIONS BY THE FILTER DIAGONALIZATION METHOD

2003 ◽  
Vol 02 (04) ◽  
pp. 497-505 ◽  
Author(s):  
VLADIMIR A. MANDELSHTAM
Keyword(s):  
Correlation Functions ◽  
Cross Correlation ◽  
Window Size ◽  
Initial State ◽  
Filter Diagonalization ◽  
Molecular Systems ◽  
Diagonalization Method ◽  
Low Dimensional ◽  
Harmonic Inversion ◽  
Filter Diagonalization Method

Harmonic inversion of Chebyshev correlation and cross-correlation functions by the filter diagonalization method (FDM) is one of the most efficient ways to accurately compute the complex spectra of low dimensional quantum molecular systems. This explains the growing popularity of the FDM in the past several years. Some of its most attractive features are the predictable convergence properties and the lack of adjusting parameters. These issues however are often misunderstood and mystified. We discuss the questions relevant to the optimal choices for the FDM parameters, such as the window size and the number of basis functions. We also demonstrate that the cross-correlation approach (using multiple initial states) is significantly more effective than the conventional autocorrelation approach (single initial state) for the common case of a non-uniform eigenvalue distribution.

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