DRAM System Simulation Speed-Up by Effective-Cycle Selection

2014 ◽  
Author(s):  
Han Chien Chiang ◽  
Mao Yin Wang ◽  
Cheng Weng Wu
Keyword(s):  
System Simulation ◽  
Simulation Speed ◽  
Speed Up

scholarly journals Efficient Closed Loop Simulation of Do-It-Yourself Artificial Pancreas Systems

2021 ◽  
pp. 193229682110322
Author(s):  
Jana Schmitzer ◽  
Carolin Strobel ◽  
Ronald Blechschmidt ◽  
Adrian Tappe ◽  
Heiko Peuscher
Keyword(s):  
Real Time ◽  
In Silico ◽  
Closed Loop ◽  
Artificial Pancreas ◽  
Virtual Patients ◽  
Simulation Speed ◽  
Virtual Population ◽  
Speed Up ◽  
Do It Yourself

Background: Numerical simulations, also referred to as in silico trials, are nowadays the first step toward approval of new artificial pancreas (AP) systems. One suitable tool to run such simulations is the UVA/Padova Type 1 Diabetes Metabolic Simulator (T1DMS). It was used by Toffanin et al. to provide data about safety and efficacy of AndroidAPS, one of the most wide-spread do-it-yourself AP systems. However, the setup suffered from slow simulation speed. The objective of this work is to speed up simulation by implementing the algorithm directly in MATLAB®/Simulink®. Method: Firstly, AndroidAPS is re-implemented in MATLAB® and verified. Then, the function is incorporated into T1DMS. To evaluate the new setup, a scenario covering 2 days in real time is run for 30 virtual patients. The results are compared to those presented in the literature. Results: Unit tests and integration tests proved the equivalence of the new implementation and the original AndroidAPS code. Simulation of the scenario required approximately 15 minutes, corresponding to a speed-up factor of roughly 1000 with respect to real time. The results closely resemble those presented by Toffanin et al. Discrepancies were to be expected because a different virtual population was considered. Also, some parameters could not be extracted from and harmonized with the original setup. Conclusions: The new implementation facilitates extensive in silico trials of AndroidAPS due to the significant reduction of runtime. This provides a cheap and fast means to test new versions of the algorithm before they are shared with the community.

Synthesis of programmable control structures for a simulation speed-up

1990 ◽  
Vol 30 (1-5) ◽  
pp. 223-229 ◽  
Author(s):  
Jean-Luc Dubois ◽  
Przemyslaw Bakowski ◽  
Adam Pawlak
Keyword(s):  
Control Structures ◽  
Simulation Speed ◽  
Speed Up

Scanning x-ray fluorescence microscopy and principal component analysis

1992 ◽  
Vol 50 (2) ◽  
pp. 1752-1753
Author(s):  
Brian Cross
Keyword(s):  
Principal Component Analysis ◽  
Fluorescence Microscopy ◽  
Spatial Resolution ◽  
High Speed ◽  
Image Acquisition ◽  
Principal Component ◽  
Fluorescence Microscope ◽  
Acquisition Rate ◽  
X Ray ◽  
Speed Up

A relatively new entry, in the field of microscopy, is the Scanning X-Ray Fluorescence Microscope (SXRFM). Using this type of instrument (e.g. Kevex Omicron X-ray Microprobe), one can obtain multiple elemental x-ray images, from the analysis of materials which show heterogeneity. The SXRFM obtains images by collimating an x-ray beam (e.g. 100 μm diameter), and then scanning the sample with a high-speed x-y stage. To speed up the image acquisition, data is acquired "on-the-fly" by slew-scanning the stage along the x-axis, like a TV or SEM scan. To reduce the overhead from "fly-back," the images can be acquired by bi-directional scanning of the x-axis. This results in very little overhead with the re-positioning of the sample stage. The image acquisition rate is dominated by the x-ray acquisition rate. Therefore, the total x-ray image acquisition rate, using the SXRFM, is very comparable to an SEM. Although the x-ray spatial resolution of the SXRFM is worse than an SEM (say 100 vs. 2 μm), there are several other advantages.

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