Graphics Accelerators as a training tool for neural networks signal synthesis of various physical principles of action

2021 ◽  
Author(s):  
V.А. Ivanov ◽  
V.V. Maksimov ◽  
K.D. Galev
Keyword(s):  
Neural Networks ◽  
Training Tool ◽  
Signal Synthesis ◽  
Physical Principles

scholarly journals Review of Neural Networks in the EEG Signal Recognition

2020 ◽  
Author(s):  
Ildar Rakhmatulin
Keyword(s):  
Neural Networks ◽  
Signal Processing ◽  
Processing Technique ◽  
Eeg Signal ◽  
Signal Recognition ◽  
Signal Processing Technique ◽  
Comprehensive Overview ◽  
The Past ◽  
Eeg Signal Processing ◽  
Physical Principles

In the last decade, unprecedented progress in the development of neural networks influenced dozens of different industries, among which are signal processing for the electroencephalography process (EEG). Electroencephalography, even though it appeared in the first half of the 20th century, to this day didn’t change the physical principles of operation. But the signal processing technique due to the use of neural networks progressed significantly in this area. Evidence for this can serve that for the past 5 years more than 1000 publications on the topic of using machine learning have been published in popular libraries. Many different models of neural networks complicate the process of understanding the real situation in this area. In this manuscript, we provided the most comprehensive overview of research where were used neural networks for EEG signal processing.

Novice's Performance and Satisfaction Improvement Through Expert Decision Support Usage

2004 ◽  
pp. 163-182
Author(s):  
Lucila Perez ◽  
Michel Plaisent ◽  
Prosper Bernard ◽  
Lassana Maguiraga
Keyword(s):  
Neural Networks ◽  
Decision Making ◽  
Decision Support ◽  
Positive Impact ◽  
Business World ◽  
New Employees ◽  
Training Tool ◽  
Treatment Groups ◽  
The Impact

Decision support technology, Expert Systems, Executives Information Systems, and Artificial Neural Networks, have been reported to be useful tools to enhance the performance of managers as they helped them to gain more knowledge, experiences, and expertise and consequently enhance the quality of the decision-making. They can also be used as a training tool to transfer the knowledge of the expert to middle and top management and thus improve the performance of new employees. This communication reports the conclusions of a study conducted to verify the impact of the use of the EDSS technology (Expert Decision Support Systems) on the performance and satisfaction of new employees in the business world. A laboratory experiment using control groups and treatment groups was held to test the research model. The results indicate that EDSS technologies do have a positive impact on the performance of the users.

scholarly journals A Physics-Driven Neural Networks-Based Simulation System (PhyNNeSS) for Multimodal Interactive Virtual Environments Involving Nonlinear Deformable Objects

2011 ◽  
Vol 20 (4) ◽  
pp. 289-308 ◽  
Author(s):  
Suvranu De ◽  
Dhannanjay Deo ◽  
Ganesh Sankaranarayanan ◽  
Venkata S. Arikatla
Keyword(s):  
Neural Networks ◽  
Real Time ◽  
Surgical Simulation ◽  
Radial Basis Function Network ◽  
Simulation System ◽  
Nonlinear Material ◽  
Deformable Objects ◽  
Interactive Simulation ◽  
Training Tool ◽  
Update Rate

While an update rate of 30 Hz is considered adequate for real-time graphics, a much higher update rate of about 1 kHz is necessary for haptics. Physics-based modeling of deformable objects, especially when large nonlinear deformations and complex nonlinear material properties are involved, at these very high rates is one of the most challenging tasks in the development of real-time simulation systems. While some specialized solutions exist, there is no general solution for arbitrary nonlinearities. In this work we present PhyNNeSS—a Physics-driven Neural Networks-based Simulation System—to address this long-standing technical challenge. The first step is an offline precomputation step in which a database is generated by applying carefully prescribed displacements to each node of the finite element models of the deformable objects. In the next step, the data is condensed into a set of coefficients describing neurons of a Radial Basis Function Network (RBFN). During real-time computation, these neural networks are used to reconstruct the deformation fields as well as the interaction forces. We present realistic simulation examples from interactive surgical simulation with real-time force feedback. As an example, we have developed a deformable human stomach model and a Penrose drain model used in the Fundamentals of Laparoscopic Surgery (FLS) training tool box. A unique computational modeling system has been developed that is capable of simulating the response of nonlinear deformable objects in real time. The method distinguishes itself from previous efforts in that a systematic physics-based precomputational step allows training of neural networks which may be used in real-time simulations. We show, through careful error analysis, that the scheme is scalable, with the accuracy being controlled by the number of neurons used in the simulation. PhyNNeSS has been integrated into SoFMIS (Software Framework for Multimodal Interactive Simulation) for general use.

X-Ray Analysis of Frozen Hydrated Sections:The Unconventional Approach

1982 ◽  
Vol 40 ◽  
pp. 754-757
Author(s):  
R. Beeuwkes ◽  
A. Saubermann ◽  
P. Echlin ◽  
S. Churchill
Keyword(s):  
Ratio Method ◽  
Frozen Sections ◽  
Technical Problems ◽  
Sample Handling ◽  
X Ray ◽  
Common Group ◽  
Ideal Method ◽  
Classic Paper ◽  
Physical Principles ◽  
The Ideal

Fifteen years ago, Hall described clearly the advantages of the thin section approach to biological x-ray microanalysis, and described clearly the ratio method for quantitive analysis in such preparations. In this now classic paper, he also made it clear that the ideal method of sample preparation would involve only freezing and sectioning at low temperature. Subsequently, Hall and his coworkers, as well as others, have applied themselves to the task of direct x-ray microanalysis of frozen sections. To achieve this goal, different methodological approachs have been developed as different groups sought solutions to a common group of technical problems. This report describes some of these problems and indicates the specific approaches and procedures developed by our group in order to overcome them. We acknowledge that the techniques evolved by our group are quite different from earlier approaches to cryomicrotomy and sample handling, hence the title of our paper. However, such departures from tradition have been based upon our attempt to apply basic physical principles to the processes involved. We feel we have demonstrated that such a break with tradition has valuable consequences.

The Basic Physical Principles of Ion, Electron, and X-Ray Detectors and Their Application to Microanalysis

1985 ◽  
Vol 43 ◽  
pp. 154-157
Author(s):  
A.J. Tousimis
Keyword(s):  
Ion Beam ◽  
Depth Resolution ◽  
Sample Surface ◽  
High Energy ◽  
X Rays ◽  
X Ray ◽  
Electrons And Ions ◽  
Spatial Depth ◽  
Minimum Surface Area ◽  
Physical Principles

An integral and of prime importance of any microtopography and microanalysis instrument system is its electron, x-ray and ion detector(s). The resolution and sensitivity of the electron microscope (TEM, SEM, STEM) and microanalyzers (SIMS and electron probe x-ray microanalyzers) are closely related to those of the sensing and recording devices incorporated with them.Table I lists characteristic sensitivities, minimum surface area and depth analyzed by various methods. Smaller ion, electron and x-ray beam diameters than those listed, are possible with currently available electromagnetic or electrostatic columns. Therefore, improvements in sensitivity and spatial/depth resolution of microanalysis will follow that of the detectors. In most of these methods, the sample surface is subjected to a stationary, line or raster scanning photon, electron or ion beam. The resultant radiation: photons (low energy) or high energy (x-rays), electrons and ions are detected and analyzed.

The Clinician Directed Hierarchy: A Clinical Training Tool

2008 ◽  
Vol 11 (2) ◽  
pp. 56-60 ◽  
Author(s):  
Jill K. Duthie
Keyword(s):  
Clinical Supervision ◽  
Clinical Training ◽  
Clinical Teaching ◽  
Clinical Skills ◽  
Clinical Settings ◽  
Specific Level ◽  
Problem Solving Skills ◽  
Training Tool ◽  
Target Behaviors ◽  
Intervention Process

Abstract Clinical supervisors in university based clinical settings are challenged by numerous tasks to promote the development of self-analysis and problem-solving skills of the clinical student (American Speech-Language-Hearing Association, ASHA, 1985). The Clinician Directed Hierarchy is a clinical training tool that assists the clinical teaching process by directing the student clinician’s focus to a specific level of intervention. At each of five levels of intervention, the clinician develops an understanding of the client’s speech/language target behaviors and matches clinical support accordingly. Additionally, principles and activities of generalization are highlighted for each intervention level. Preliminary findings suggest this is a useful training tool for university clinical settings. An essential goal of effective clinical supervision is the provision of support and guidance in the student clinician’s development of independent clinical skills (Larson, 2007). The student clinician is challenged with identifying client behaviors in the therapeutic process and learning to match his or her instructions, models, prompts, reinforcement, and use of stimuli appropriately according to the client’s needs. In addition, the student clinician must be aware of techniques in the intervention process that will promote generalization of new communication behaviors. Throughout the intervention process, clinicians are charged with identifying appropriate target behaviors, quantifying the progress of the client’s acquisition of the targets, and making adjustments within and between sessions as necessary. Central to the development of clinical skills is the feedback provided by the clinical supervisor (Brasseur, 1989; Moss, 2007). Particularly in the early stages of clinical skills development, the supervisor is challenged with addressing numerous aspects of clinical performance and awareness, while ensuring the client’s welfare (Moss). To address the management of clinician and client behaviors while developing an understanding of the clinical intervention process, the University of the Pacific has developed and begun to implement the Clinician Directed Hierarchy.

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