scholarly journals Noninvasive intracranial pressure real-time waveform analysis monitor during prostatectomy robotic surgery and Trendelenburg position: case report

2021 ◽  
Author(s):  
Gabriela Tognini Saba ◽  
Vinicius Caldeira Quintão ◽  
Suely Pereira Zeferino ◽  
Claudia Marquez Simões ◽  
Rafael Ferreira Coelho ◽  
...  
Keyword(s):  
Case Report ◽  
Intracranial Pressure ◽  
Robotic Surgery ◽  
Real Time ◽  
Waveform Analysis ◽  
Trendelenburg Position ◽  
Time Waveform

A Critical Period of Ventricular Fibrillation More Susceptible to Defibrillation: Real-Time Waveform Analysis Using a Single ECG Lead

1996 ◽  
Vol 19 (4) ◽  
pp. 418-430 ◽  
Author(s):  
PENG-WIE E. HSIA ◽  
SYLVIA FRERK ◽  
CYNTHIA A. ALLEN ◽  
ROBERT M. WISE ◽  
NERI M. COHEN ◽  
...  
Keyword(s):  
Ventricular Fibrillation ◽  
Real Time ◽  
Critical Period ◽  
Waveform Analysis ◽  
Time Waveform

The Use of Real-Time Visual Displays of Speech in the Training of a Profoundly Deaf, Nonspeaking Child: A Case Report

1971 ◽  
Vol 36 (3) ◽  
pp. 397-409 ◽  
Author(s):  
Rachel E. Stark
Keyword(s):  
Case Report ◽  
Real Time ◽  
Speech Production ◽  
Behavior Problem ◽  
Deaf Children ◽  
Developmental Sequence ◽  
Visual Displays ◽  
Individual Instruction ◽  
Academic Record ◽  
Extract Information

Real-time amplitude contour and spectral displays were used in teaching speech production skills to a profoundly deaf, nonspeaking boy. This child had a visual attention problem, a behavior problem, and a poor academic record. In individual instruction, he was first taught to produce features of speech, for example, friction, nasal, and stop, which are present in vocalizations of 6- to 9-month-old infants, and then to combine these features in syllables and words. He made progress in speech, although sign language and finger spelling were taught at the same time. Speech production skills were retained after instruction was terminated. The results suggest that deaf children are able to extract information about the features of speech from visual displays, and that a developmental sequence should be followed as far as possible in teaching speech production skills to them.

scholarly journals Real-Time Instrument Segmentation in Robotic Surgery Using Auxiliary Supervised Deep Adversarial Learning

2019 ◽  
Vol 4 (2) ◽  
pp. 2188-2195 ◽  
Author(s):  
Mobarakol Islam ◽  
Daniel Anojan Atputharuban ◽  
Ravikiran Ramesh ◽  
Hongliang Ren
Keyword(s):  
Robotic Surgery ◽  
Real Time ◽  
Adversarial Learning

scholarly journals Spiking Neural Network with Linear Computational Complexity for Waveform Analysis in Amperometry

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 3276
Author(s):  
Szymon Szczęsny ◽  
Damian Huderek ◽  
Łukasz Przyborowski
Keyword(s):  
Neural Network ◽  
Learning Algorithm ◽  
Waveform Analysis ◽  
Spiking Neural Network ◽  
Synaptic Connections ◽  
Practical Application ◽  
Accuracy Parameter ◽  
The Stability ◽  
Time Waveform ◽  
Network Mapping

The paper describes the architecture of a Spiking Neural Network (SNN) for time waveform analyses using edge computing. The network model was based on the principles of preprocessing signals in the diencephalon and using tonic spiking and inhibition-induced spiking models typical for the thalamus area. The research focused on a significant reduction of the complexity of the SNN algorithm by eliminating most synaptic connections and ensuring zero dispersion of weight values concerning connections between neuron layers. The paper describes a network mapping and learning algorithm, in which the number of variables in the learning process is linearly dependent on the size of the patterns. The works included testing the stability of the accuracy parameter for various network sizes. The described approach used the ability of spiking neurons to process currents of less than 100 pA, typical of amperometric techniques. An example of a practical application is an analysis of vesicle fusion signals using an amperometric system based on Carbon NanoTube (CNT) sensors. The paper concludes with a discussion of the costs of implementing the network as a semiconductor structure.

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