scholarly journals Solving a $$6120$$ -bit DLP on a Desktop Computer

2014 ◽  
pp. 136-152 ◽  
Author(s):  
Faruk Göloğlu ◽  
Robert Granger ◽  
Gary McGuire ◽  
Jens Zumbrägel
Keyword(s):  
Desktop Computer

Training Situation Awareness and Adaptive Decision-Making Skills Using a Desktop Computer Simulation

2009 ◽  
Author(s):  
Robert J. Pleban ◽  
Jennifer S. Tucker ◽  
Vanessa Johnson Katie /Gunther ◽  
Thomas R. Graves
Keyword(s):  
Decision Making ◽  
Computer Simulation ◽  
Situation Awareness ◽  
Desktop Computer ◽  
Adaptive Decision Making

Nvidia CUDA parallel processing of large FDTD meshes in a desktop computer

2020 ◽  
Author(s):  
Robert Calatayud ◽  
Enrique Navarro-Modesto ◽  
Enrique A. Navarro-Camba ◽  
Nagula T. Sangary
Keyword(s):  
Parallel Processing ◽  
Desktop Computer ◽  
Nvidia Cuda

scholarly journals Supervised dimensionality reduction for big data

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Joshua T. Vogelstein ◽  
Eric W. Bridgeford ◽  
Minh Tang ◽  
Da Zheng ◽  
Christopher Douville ◽  
...  
Keyword(s):  
Dimensionality Reduction ◽  
Data Science ◽  
Real Data ◽  
Low Rank ◽  
Conditional Moment ◽  
Desktop Computer ◽  
Reduction Techniques ◽  
Reduction Methods ◽  
The Individual ◽  
Low Dimensional

AbstractTo solve key biomedical problems, experimentalists now routinely measure millions or billions of features (dimensions) per sample, with the hope that data science techniques will be able to build accurate data-driven inferences. Because sample sizes are typically orders of magnitude smaller than the dimensionality of these data, valid inferences require finding a low-dimensional representation that preserves the discriminating information (e.g., whether the individual suffers from a particular disease). There is a lack of interpretable supervised dimensionality reduction methods that scale to millions of dimensions with strong statistical theoretical guarantees. We introduce an approach to extending principal components analysis by incorporating class-conditional moment estimates into the low-dimensional projection. The simplest version, Linear Optimal Low-rank projection, incorporates the class-conditional means. We prove, and substantiate with both synthetic and real data benchmarks, that Linear Optimal Low-Rank Projection and its generalizations lead to improved data representations for subsequent classification, while maintaining computational efficiency and scalability. Using multiple brain imaging datasets consisting of more than 150 million features, and several genomics datasets with more than 500,000 features, Linear Optimal Low-Rank Projection outperforms other scalable linear dimensionality reduction techniques in terms of accuracy, while only requiring a few minutes on a standard desktop computer.

Inversion of slingram electromagnetic induction data using a Born approximation

Geophysics ◽  
2013 ◽  
Vol 78 (4) ◽  
pp. E201-E212 ◽  
Author(s):  
Jochen Kamm ◽  
Michael Becken ◽  
Laust B. Pedersen
Keyword(s):  
Half Space ◽  
Born Approximation ◽  
High Efficiency ◽  
Three Dimensions ◽  
Background Model ◽  
Desktop Computer ◽  
Near Surface ◽  
Homogeneous Half Space ◽  
Integral Equation Formulation ◽  
Forward Operator

We present an efficient approximate inversion scheme for near-surface loop-loop EM induction data (slingram) that can be applied to obtain 2D or 3D models on a normal desktop computer. Our approach is derived from a volume integral equation formulation with an arbitrarily conductive homogeneous half-space as a background model. The measurements are not required to fulfill the low induction number condition (low frequency and conductivity). The high efficiency of the method is achieved by invoking the Born approximation around a half-space background. The Born approximation renders the forward operator linear. The choice of a homogeneous half-space yields closed form expressions for the required electromagnetic normal fields. It also yields a translationally invariant forward operator, i.e., a highly redundant Jacobian. In connection with the application of a matrix-free conjugate gradient method, this allows for very low memory requirements during the inversion, even in three dimensions. As a consequence of the Born approximation, strong conductive deviations from the background model are underestimated. Highly resistive anomalies are in principle overestimated, but at the same time difficult to resolve with induction methods. In the case of extreme contrasts, our forward model may fail in simultaneously explaining all the data collected. We applied the method to EM34 data from a profile that has been extensively studied with other electromagnetic methods and compare the results. Then, we invert three conductivity maps from the same area in a 3D inversion.

scholarly journals The Application of a Computerized Cognitive Screening Tool in Naval Aviators

2021 ◽  
Vol 186 (Supplement_1) ◽  
pp. 198-204
Author(s):  
Shawnna M Chee ◽  
Veronica E Bigornia ◽  
Daniel L Logsdon
Keyword(s):  
Repeated Measures ◽  
High Performance ◽  
Mixed Model ◽  
Screening Tool ◽  
Federal Aviation Administration ◽  
Control Group ◽  
Operational Environment ◽  
Desktop Computer ◽  
Naval Aviators ◽  
High Performance Aircraft

ABSTRACT Introduction The CogScreen-Aeromedical Edition (CogScreen-AE) is a computerized neurocognitive assessment screening tool developed for the Federal Aviation Administration as a rapid, reliable means of measuring neurocognitive deficiency in civilian airline pilots. This has potential use and assessment of military aviators flying high performance aircraft under extreme conditions; however, no data exist on how the dynamic flight environment affects CogScreen-AE scores. The objectives of this study were to determine what changes in performance on CogScreen-AE scores are seen post-flight in Naval Aviators flying high performance aircraft and to determine the potential for use of CogScreen-AE as a screening tool to evaluate degree of impairment, recovery from neurological illness, and return to duty status of a military aviator. Materials and Methods Repeated measures, within-subjects experimental design with three CogScreen-AE administrations—introduction session, preflight session, and postflight session. An experimental study group was exposed to dynamic flight between preflight and postflight sessions, while a control group flew a desktop computer flight simulator between sessions. Data were analyzed by mixed model ANOVA using Statistical Package for the Social Sciences to compare CogScreen-AE pre- and postflight performance on 5 composite scores of variables that account for 45% of the variance in predicting flight performance. Results Preflight versus postflight scores demonstrated no significant differences in performance attributable to flight in high performance aircraft. Conclusions The CogScreen-AE performance is shown to be consistent preflight to post-flight. These data show that CogScreen-AE may be a reliable clinical instrument for assessing aviators’ cognitive function with regard to return to flight duty decision-making. We anticipate future work in determining how CogScreen-AE can be utilized in the operational environment and documenting recovery from neurologic illness.

Concept and Applications of M-Commerce in India

2021 ◽  
Vol 1 (3) ◽  
pp. 58-60
Author(s):  
Katanakal Sarada ◽  
◽  
Dr. K. Nirmalamma ◽  
◽  
Keyword(s):  
Online Shopping ◽  
Main Idea ◽  
Mobile App ◽  
Smart Phones ◽  
The Internet ◽  
Portable Devices ◽  
Market Place ◽  
Desktop Computer ◽  
Goods And Services ◽  
The People

Mobile commerce is the buying and selling of goods and Services through wireless handled devices such as smart phones and tablets etc. Ecommerce Users to access M-commerce enables online shopping platforms without needing to use & a desktop computer. For example, purchase and sale of products. Online like banking and paying bills. (Virtual market place apps the Amazon mobile App, Android pay, Samsung pay etc...) The main idea behind M. commerce Is to enable various applications and services available on the internet to portable devices (mobiles, laptops, tables etc.) to overcome the constraints of a desktop computer. M commerce aims Serve all information and material needs of the people in a convenient and easy way.

Study on Temperature Distribution of Pebble-Bed HTR Fuel Element by Using Boundary Element Method

2013 ◽  
Author(s):  
Haitao Wang ◽  
Xin Wang
Keyword(s):  
Fuel Element ◽  
Boundary Element ◽  
Large Scale ◽  
Maximum Temperature ◽  
Element Formulation ◽  
Desktop Computer ◽  
Pebble Bed ◽  
Graphite Matrix ◽  
Fuel Particles ◽  
Fuel Elements

Spherical fuel elements with a diameter of 60mm are basic units of the nuclear fuel for the pebble-bed high temperature gas-cooled reactor (HTR). Each fuel element is treated as a graphite matrix containing around 10,000 randomly distributed fuel particles. The essential safety concept of the pebble-bed HTR is based on the objective that maximum temperature of the fuel particles does not exceed the design value. In this paper, a microstructure-based boundary element model is proposed for the large-scale thermal analysis of a spherical fuel element. This model presents detailed structural information of a large number of coated fuel particles dispersed in a spherical graphite matrix in order that temperature distributions at the level of fuel particles can be evaluated. The model is meshed with boundary elements in conjunction with the fast multipole method (FMM) in order that such large-scale computation is performed only in a personal desktop computer. Taking advantage of the fact that fuel particles are of the same shape, a similar sub-domain approach is used to establish the temperature translation mechanism between various layers of each fuel particle and to simplify the associated boundary element formulation. The numerical results demonstrate large-scale capacity of the proposed method for the multi-level temperature evaluation of the pebble-bed HTR fuel elements.

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