scholarly journals Novel Instrumentation for Real-Time Monitoring Using Miniaturized Flow Systems with Integrated Biosensors

1997 ◽  
Vol 34 (3) ◽  
pp. 291-302 ◽  
Author(s):  
R Freaney ◽  
A McShane ◽  
T V Keaveny ◽  
M McKenna ◽  
K Rabenstein ◽  
...  
Keyword(s):  
Real Time ◽  
Response Times ◽  
Computer Software ◽  
Dead Volume ◽  
System Response ◽  
Small Scale ◽  
Biosensor Array ◽  
In Vivo Testing ◽  
On Line

A prototype miniaturized Total Chemical Analysis System (μTAS) has been developed and applied to on-line monitoring of glucose and lactate in the core blood of anaesthetized dogs. The system consists of a highly efficient microdialysis sampling interface sited in a small-scale extracorporeal shunt circuit (‘MiniShunt’), a silicon machined microflow manifold and integrated biosensor array for glucose and lactate detection with associated computer software for analytical process control. During in-vivo testing the device allowed real-time on-screen monitoring of glucose and lactate with system response times of less than 5 min, made possible by the small dead volume of the microflow system. On-line glucose and lactate measurements were made in the basal state as well as during intravenous infusion of glucose or lactate. The prototype μTAS is currently suitable for trend monitoring but refinements are necessary before application of the system for determination of individual lactate values.

scholarly journals Hydrogel-coating improves the in-vivo stability of electrochemical aptamer-based biosensors

2020 ◽  
Author(s):  
Shaoguang Li ◽  
Jun Dai ◽  
Man Zhu ◽  
Netzahualcóyotl Arroyo-Currás ◽  
Hongxing Li ◽  
...  
Keyword(s):  
Real Time ◽  
Response Times ◽  
Protective Layer ◽  
Drift Correction ◽  
Living Body ◽  
Agarose Hydrogel ◽  
Improved Stability ◽  
Varying Environments

AbstractThe ability to track the levels of specific molecules, such as drugs, metabolites, and biomarkers, in the living body, in real time and for long durations would improve our understanding of health and our ability to diagnose, treat and monitor disease. To this end, we are developing electrochemical aptamer-based (E-AB) biosensors, a general platform supporting high-frequency, real-time molecular measurements in the living body. Here we report that the addition of an agarose hydrogel protective layer to E-AB sensors significantly improves their baseline stability when deployed in the complex, highly time-varying environments found in vivo. The improved stability is sufficient that these hydrogel-protected sensors achieved good baseline stability when deployed in situ in the veins, muscles, bladder, or tumors of living rats without the use of the drift correction approaches traditionally required in such placements. Finally, this improved stability is achieved without any significant, associated “costs” in terms of detection limits, response times, or biocompatibility.

IMPLEMENTING BACK-PROPAGATION- THROUGH-TIME LEARNING ALGORITHM USING CELLULAR NEURAL NETWORKS

1999 ◽  
Vol 09 (06) ◽  
pp. 1041-1074 ◽  
Author(s):  
TAO YANG ◽  
LEON O. CHUA
Keyword(s):  
Real Time ◽  
Time Complexity ◽  
Large Scale ◽  
Learning Algorithm ◽  
State Of The Art ◽  
Back Propagation ◽  
Cellular Neural Network ◽  
Small Scale ◽  
Von Neumann ◽  
On Line

In a programmable (multistage) cellular neural network (CNN) structure, the CPU is a CNN universal chip which supports massively parallel computations on patterns and images, including videos. In this paper, we decompose the structure of a class of simultaneous recurrent networks (SRN) into a CNN program and run it on a von Neumann-like stored program CNN structure. To train the SRN, we map the back-propagation-through-time (BTT) learning algorithm into a sequence of CNN subroutines to achieve real-time performance via a CNN universal chip. By computing in parallel, the CNN universal chip can be programmed to implement in real time the BTT learning algorithm, which has a very high time complexity. An estimate of the time complexity of the BTT learning algorithm based on the CNN universal chip is presented. For small-scale problems, our simulation results show that a CNN implementation of the BTT learning algorithm for a two-dimensional SRN is at least 10,000 times faster than that based on state-of-the-art sequential workstations. For the few large-scale problems which we have so far simulated, the CNN implemented BTT learning algorithm maintained virtually the same time complexity with a learning time of a few seconds, while those implemented on state-of-the-art sequential workstations dramatically increased their time complexity, often requiring several days of running time. Several examples are presented to demonstrate how efficiently a CNN universal chip can speed up the learning algorithm for both off-line and on-line applications.

scholarly journals Predicting performance indicators with ANNs for AI-based online scheduling in dynamically interconnected assembly systems

2021 ◽  
Author(s):  
Amon Göppert ◽  
Leon Mohring ◽  
Robert H. Schmitt
Keyword(s):  
Real Time ◽  
Performance Indicators ◽  
Response Times ◽  
Computing Time ◽  
Discrete Event ◽  
Online Scheduling ◽  
System Response ◽  
Assembly Systems ◽  
Discrete Event Simulations ◽  
Predicting Performance

AbstractMass customization demands shorter manufacturing system response times due to frequent product changes. This increase in system dynamics imposes additional flexibility requirements especially on assembly processes, as complexity accumulates in this last step of value creation. Flexible and dynamically interconnected assembly systems can meet the increased requirements as opposed to traditional dedicated assembly line approaches. The high complexity and dynamical environment in these kinds of systems lead to the demand for real-time online control and scheduling solutions. Within the decision-making of online scheduling, the capability of predicting the consequences of available actions is crucial. In real-time environments, running extensive discrete-event simulations to evaluate how actions unfold requires too much computing time. Artificial neural networks (ANN) are a viable alternative to quickly evaluate the potential future performance value of a production state for online production planning and control. They can predict performance indicators such as the expected makespan given the current production status. Leveraging recent advances in artificial intelligence (AI) game algorithms, an assembly control system based on Google DeepMind’s AlphaZero was created. Specifically, an ANN is incorporated into the approach that suggests favorable job routing decisions and predicts the value of actions. The results show that the trained network can predict favorable actions with an accuracy of over 95% and estimate the makespan with an error smaller than 3%.

scholarly journals Wireless, implantable catheter-type oximeter designed for cardiac oxygen saturation

2021 ◽  
Vol 7 (7) ◽  
pp. eabe0579
Author(s):  
Wei Lu ◽  
Wubin Bai ◽  
Hao Zhang ◽  
Chenkai Xu ◽  
Antonio M. Chiarelli ◽  
...  
Keyword(s):  
Real Time ◽  
High Performance ◽  
Measurement Accuracy ◽  
Optical Components ◽  
Freedom Of Movement ◽  
Accuracy And Precision ◽  
In Vivo Testing ◽  
Supply Systems

Accurate, real-time monitoring of intravascular oxygen levels is important in tracking the cardiopulmonary health of patients after cardiothoracic surgery. Existing technologies use intravascular placement of glass fiber-optic catheters that pose risks of blood vessel damage, thrombosis, and infection. In addition, physical tethers to power supply systems and data acquisition hardware limit freedom of movement and add clutter to the intensive care unit. This report introduces a wireless, miniaturized, implantable optoelectronic catheter system incorporating optical components on the probe, encapsulated by soft biocompatible materials, as alternative technology that avoids these disadvantages. The absence of physical tethers and the flexible, biocompatible construction of the probe represent key defining features, resulting in a high-performance, patient-friendly implantable oximeter that can monitor localized tissue oxygenation, heart rate, and respiratory activity with wireless, real-time, continuous operation. In vitro and in vivo testing shows that this platform offers measurement accuracy and precision equivalent to those of existing clinical standards.

Effect of Scale on the Complexity of Snowmelt Systems

1981 ◽  
Vol 12 (4-5) ◽  
pp. 225-234 ◽  
Author(s):  
L.N. Braun ◽  
H.O. Slaymaker
Keyword(s):  
Stream Flow ◽  
Response Times ◽  
System Response ◽  
Small Scale ◽  
Ice Storage ◽  
Coast Mountains ◽  
Flow Response ◽  
Internal Homogeneity ◽  
A Site ◽  
Meso Scale

During the melt season of 1978, the nature of snow and ice storage, the energy sources and the stream flow response were investigated at a site, a small-scale and a meso-scale watershed in the Coast Mountains of British Columbia, Canada. Differences in system internal homogeneity and system response times at each of these scales indicated the usefulness of this empirical classification. It is also demonstrated that different methods of analysis of snowmelt systems are appropriate at the different scales.

Software pattern recognition applied to nanodiffraction patterns from a STEM instrument

1986 ◽  
Vol 44 ◽  
pp. 694-695
Author(s):  
G.Y. Fan ◽  
J.M. Cowley
Keyword(s):  
Image Processing ◽  
Pattern Recognition ◽  
Computer Software ◽  
Processing System ◽  
Light Level ◽  
Host Computer ◽  
Low Light ◽  
Image Processing System ◽  
Recent Developments ◽  
On Line

In recent developments, the ASU HB5 has been modified so that the timing, positioning, and scanning of the finely focused electron probe can be entirely controlled by a host computer. This made the asynchronized handshake possible between the HB5 STEM and the image processing system which consists of host computer (PDP 11/34), DeAnza image processor (IP 5000) which is interfaced with a low-light level TV camera, array processor (AP 400) and various peripheral devices. This greatly facilitates the pattern recognition technique initiated by Monosmith and Cowley. Software called NANHB5 is under development which, instead of employing a set of photo-diodes to detect strong spots on a TV screen, uses various software techniques including on-line fast Fourier transform (FFT) to recognize patterns of greater complexity, taking advantage of the sophistication of our image processing system and the flexibility of computer software.

Hepatobiliary Kinetics of 113mIn-Phenolphthalexon

1981 ◽  
Vol 20 (02) ◽  
pp. 90-93
Author(s):  
P.B. Parab ◽  
U.R. Raikar ◽  
R.D. Ganatra ◽  
M. C. Patel
Keyword(s):  
Biliary Excretion ◽  
Iminodiacetic Acid ◽  
Organ Distribution ◽  
Liver Uptake ◽  
On Line ◽  
Rapid Clearance ◽  
Chemical Purity ◽  
Kinetics Of ◽  
High Liver

Phenolphthalexon, a compound with iminodiacetic acid as a functional group, has been labelled with 113mIn to high chemical purity and its usefulness in studies of biliary excretion patency has been studied. Organ distribution of 113mIn-phenolphthalexon in mice was characterized by high liver uptake (50.8% of the administered dose after 5 min) and rapid clearance through the gall bladder. An animal model for studying obstruction of biliary excretion has been developed. Data on the kinetics of the radiopharmaceutical were obtained by collecting in-vivo data through an on-line computer.

Adaptive Signal Analysis and Interpretation for Real-time Intelligent Patient Monitoring

1994 ◽  
Vol 33 (01) ◽  
pp. 60-63 ◽  
Author(s):  
E. J. Manders ◽  
D. P. Lindstrom ◽  
B. M. Dawant
Keyword(s):  
Computer Architecture ◽  
Real Time ◽  
Data Abstraction ◽  
Monitoring Strategy ◽  
Intelligent Monitoring ◽  
Patient Status ◽  
On Line ◽  
Main Components ◽  
And Control ◽  
Adaptive Signal

Abstract:On-line intelligent monitoring, diagnosis, and control of dynamic systems such as patients in intensive care units necessitates the context-dependent acquisition, processing, analysis, and interpretation of large amounts of possibly noisy and incomplete data. The dynamic nature of the process also requires a continuous evaluation and adaptation of the monitoring strategy to respond to changes both in the monitored patient and in the monitoring equipment. Moreover, real-time constraints may imply data losses, the importance of which has to be minimized. This paper presents a computer architecture designed to accomplish these tasks. Its main components are a model and a data abstraction module. The model provides the system with a monitoring context related to the patient status. The data abstraction module relies on that information to adapt the monitoring strategy and provide the model with the necessary information. This paper focuses on the data abstraction module and its interaction with the model.

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