Real-time waveform analysis of multichannel nerve impulses with a multimicroprocessor system

1985 ◽  
Vol 23 (1) ◽  
pp. 23-27 ◽  
Author(s):  
M. Ikeda ◽  
N. Hoshimiya
Keyword(s):  
Real Time ◽  
Waveform Analysis ◽  
Nerve Impulses ◽  
Time Waveform

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

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.

Toward Polymorphic Internet of Things Receivers Through Real-Time Waveform-Level Deep Learning

2022 ◽  
Vol 25 (3) ◽  
pp. 28-33
Author(s):  
Francesco Restuccia ◽  
Tommaso Melodia
Keyword(s):  
Deep Learning ◽  
Internet Of Things ◽  
Real Time ◽  
Software Defined Radio ◽  
Wireless Systems ◽  
Physical World ◽  
Wireless Protocols ◽  
Key Innovation ◽  
Custom Made ◽  
Time Waveform

Wireless systems such as the Internet of Things (IoT) are changing the way we interact with the cyber and the physical world. As IoT systems become more and more pervasive, it is imperative to design wireless protocols that can effectively and efficiently support IoT devices and operations. On the other hand, today's IoT wireless systems are based on inflexible designs, which makes them inefficient and prone to a variety of wireless attacks. In this paper, we introduce the new notion of a deep learning-based polymorphic IoT receiver, able to reconfigure its waveform demodulation strategy itself in real time, based on the inferred waveform parameters. Our key innovation is the introduction of a novel embedded deep learning architecture that enables the solution of waveform inference problems, which is then integrated into a generalized hardware/software architecture with radio components and signal processing. Our polymorphic wireless receiver is prototyped on a custom-made software-defined radio platform. We show through extensive over-the-air experiments that the system achieves throughput within 87% of a perfect-knowledge Oracle system, thus demonstrating for the first time that polymorphic receivers are feasible.

Real-time waveform modulator based on dispersion engineering of magnetic surface plasmons

2018 ◽  
Vol 123 (24) ◽  
pp. 245106
Author(s):  
Zhuoluo Wang ◽  
Jiafu Wang ◽  
Xin Wang ◽  
Jie Yang ◽  
Yaodong Zhao ◽  
...  
Keyword(s):  
Real Time ◽  
Surface Plasmons ◽  
Magnetic Surface ◽  
Dispersion Engineering ◽  
Time Waveform

Real-Time Waveform Prototyping

2019 ◽  
Author(s):  
Martin Danneberg ◽  
Zhongju Li ◽  
Paul Kuhne ◽  
Ahmad Nimr ◽  
Shahab Ehsanfar ◽  
...  
Keyword(s):  
Real Time ◽  
Time Waveform
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