Handling EEG artifacts and searching individually optimal experimental parameter in real time: a system development and demonstration

2021 ◽  
Author(s):  
Guang Ouyang ◽  
Joseph Dien ◽  
Romy Lorenz
Keyword(s):  
Real Time ◽  
Biomarker Discovery ◽  
Bayesian Optimization ◽  
Clinical Population ◽  
Research Approach ◽  
Stimulus Interval ◽  
Efficient System ◽  
Cost Efficient ◽  
Handling Method ◽  
Eeg Artifacts

Abstract Objective. Neuroadaptive paradigms that systematically assess ERP features across many different experimental parameters have the potential to improve the generalizability of ERP findings and may help to accelerate ERP-based biomarker discovery by identifying the exact experimental conditions for which ERPs differ most for a certain clinical population. Obtaining robust and reliable ERPs online is a prerequisite for ERP-based neuroadaptive research. One of the key steps involved is to correctly isolate EEG artifacts in real time because they contribute a large amount of variance that, if not removed, will greatly distort the ERP obtained. Another key factor of concern is the computational cost of the online artifact handling method. This work aims to develop and validate a cost-efficient system to support ERP-based neuroadaptive research. Approach. We developed a simple online artifact handling method, single trial PCA-based artifact removal (SPA), based on variance distribution dichotomies to distinguish between artifacts and neural activity. We then applied this method in an ERP-based neuroadaptive paradigm in which Bayesian optimization was used to search individually optimal inter-stimulus-interval (ISI) that generates ERP with the highest signal-to-noise ratio. Main results. SPA was compared to other offline and online algorithms. The results showed that SPA exhibited good performance in both computational efficiency and preservation of ERP pattern. Based on SPA, the Bayesian optimization procedure was able to quickly find individually optimal ISI. Significance. The current work presents a simple yet highly cost-efficient method that has been validated in its ability to extract ERP, preserve ERP effects, and better support ERP-based neuroadaptive paradigm.

scholarly journals High-precision and cost-efficient sequencing for real-time COVID-19 surveillance

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Sung Yong Park ◽  
Gina Faraci ◽  
Pamela M. Ward ◽  
Jane F. Emerson ◽  
Ha Youn Lee
Keyword(s):  
Los Angeles ◽  
Whole Genome Sequencing ◽  
Real Time ◽  
Genome Sequencing ◽  
High Precision ◽  
High Throughput Sequencing ◽  
Whole Genome ◽  
Sequencing Data ◽  
Public Health Response ◽  
Cost Efficient

AbstractCOVID-19 global cases have climbed to more than 33 million, with over a million total deaths, as of September, 2020. Real-time massive SARS-CoV-2 whole genome sequencing is key to tracking chains of transmission and estimating the origin of disease outbreaks. Yet no methods have simultaneously achieved high precision, simple workflow, and low cost. We developed a high-precision, cost-efficient SARS-CoV-2 whole genome sequencing platform for COVID-19 genomic surveillance, CorvGenSurv (Coronavirus Genomic Surveillance). CorvGenSurv directly amplified viral RNA from COVID-19 patients’ Nasopharyngeal/Oropharyngeal (NP/OP) swab specimens and sequenced the SARS-CoV-2 whole genome in three segments by long-read, high-throughput sequencing. Sequencing of the whole genome in three segments significantly reduced sequencing data waste, thereby preventing dropouts in genome coverage. We validated the precision of our pipeline by both control genomic RNA sequencing and Sanger sequencing. We produced near full-length whole genome sequences from individuals who were COVID-19 test positive during April to June 2020 in Los Angeles County, California, USA. These sequences were highly diverse in the G clade with nine novel amino acid mutations including NSP12-M755I and ORF8-V117F. With its readily adaptable design, CorvGenSurv grants wide access to genomic surveillance, permitting immediate public health response to sudden threats.

scholarly journals Microfluidic Airborne Metal Particle Sensor Using Oil Microcirculation for Real-Time and Continuous Monitoring of Metal Particle Emission

Micromachines ◽  
2021 ◽  
Vol 12 (7) ◽  
pp. 825
Author(s):  
Jong-Seo Yoon ◽  
Jiwon Park ◽  
Hye-Rin Ahn ◽  
Seong-Jae Yoo ◽  
Yong-Jun Kim
Keyword(s):  
Real Time ◽  
Metal Particle ◽  
Continuous Monitoring ◽  
Metal Particles ◽  
Minimum Size ◽  
Capacitive Sensing ◽  
Test Bed ◽  
Optimal Frequency ◽  
Site Monitoring ◽  
Cost Efficient

Airborne metal particles (MPs; particle size > 10 μm) in workplaces result in a loss in production yield if not detected in time. The demand for compact and cost-efficient MP sensors to monitor airborne MP generation is increasing. However, contemporary instruments and laboratory-grade sensors exhibit certain limitations in real-time and on-site monitoring of airborne MPs. This paper presents a microfluidic MP detection chip to address these limitations. By combining the proposed system with microcirculation-based particle-to-liquid collection and a capacitive sensing method, the continuous detection of airborne MPs can be achieved. A few microfabrication processes were realized, resulting in a compact system, which can be easily replaced after contamination with a low-priced microfluidic chip. In our experiments, the frequency-dependent capacitive changes were characterized using MP (aluminum) samples (sizes ranging from 10 μm to 40 μm). Performance evaluation of the proposed system under test-bed conditions indicated that it is capable of real-time and continuous monitoring of airborne MPs (minimum size 10 μm) under an optimal frequency, with superior sensitivity and responsivity. Therefore, the proposed system can be used as an on-site MP sensor for unexpected airborne MP generation in precise manufacturing facilities where metal sources are used.

Linked Energy Systems for Production Sites of the Future

2013 ◽  
Vol 769 ◽  
pp. 319-326 ◽  
Author(s):  
Martin Beck ◽  
Tilo Sielaff
Keyword(s):  
Energy Efficiency ◽  
Energy Efficient ◽  
Energy Demand ◽  
Energy System ◽  
Efficient System ◽  
Industrial Enterprises ◽  
Future Production ◽  
Cost Efficient ◽  
System Configurations ◽  
Energy Network

Industrial enterprises are increasingly driven to tap the potentials of energy efficiency in existing and future production sites. The challenge is to identify cost-efficient levers for a low energy demand in the linked energy system of production machines and peripheral devices. Considering enabling technologies for energy efficiency and energy recovery in a cascaded energy network with energy storages this paper presents an approach towards energy and cost-efficient system configurations for production sites. An outlook will be given on the research center eta-factory for energy efficient factories at the PTW, TU Darmstadt.

scholarly journals ARTIST - a reAl-time low-effoRt mulTi-entity Interaction System for creaTing reusable and optimized MR experiences

2019 ◽  
Vol 5 ◽  
Author(s):  
Konstantinos Kotis
Keyword(s):  
Real Time ◽  
Development Time ◽  
Mixed Reality ◽  
Research Approach ◽  
The Novel ◽  
High Quality ◽  
Related Data ◽  
Novel Methods ◽  
Novel Concept ◽  
Heterogeneous Resources

ARTIST is a research approach introducing novel methods for real-time multi-entity interaction between human and non-human entities, to create reusable and optimized Mixed Reality (MR) experiences with low-effort, towards a Shared MR Experiences Ecosystem (SMRE2). As a result, ARTIST delivers high quality MR experiences, facilitating the interaction between a variety of entities which interact in a virtual and symbiotic way within a mega, virtual and fully-experiential world. Specifically, ARTIST aims to develop novel methods for low-effort (code-free) implementation and deployment of open and reusable MR content, applications and tools, introducing the novel concept of an Experience as a Trajectory (EaaT). In addition, ARTIST will provide tools for the tracking, monitoring and analysis of user behaviour and their interaction with the environment and with other users, towards optimizing MR experiences by recommending their reconfiguration, dynamically (at run-time) or statically (at development time). Finally, it will provide tools for synthesizing experiences into new mega and still reconfigurable EaaTs, enhancing them at the same time using semantically integrated related data/information available in disparate and heterogeneous resources.

scholarly journals A reAl-time low-effoRt mulTi-entity Interaction System for creaTing reusable and optimized MR experiences

2019 ◽  
Vol 5 ◽  
Author(s):  
Konstantinos Kotis
Keyword(s):  
Real Time ◽  
Development Time ◽  
Mixed Reality ◽  
Research Approach ◽  
The Novel ◽  
High Quality ◽  
Related Data ◽  
Novel Methods ◽  
Novel Concept ◽  
Heterogeneous Resources

ARTIST is a research approach introducing novel methods for real-time multi-entity interaction between human and non-human entities, to create reusable and optimized Mixed Reality (MR) experiences with low-effort, towards a Shared MR Experiences Ecosystem (SMRE2). As a result, ARTIST delivers high quality MR experiences, facilitating the interaction between a variety of entities which interact in a virtual and symbiotic way within a mega, virtual and fully-experiential world. Specifically, ARTIST aims to develop novel methods for low-effort (code-free) implementation and deployment of open and reusable MR content, applications and tools, introducing the novel concept of an Experience as a Trajectory (EaaT). In addition, ARTIST will provide tools for the tracking, monitoring and analysis of user behaviour and their interaction with the environment and with other users, towards optimizing MR experiences by recommending their reconfiguration, dynamically (at run-time) or statically (at development time). Finally, it will provide tools for synthesizing experiences into new mega and still reconfigurable EaaTs, enhancing them at the same time using semantically integrated related data/information available in disparate and heterogeneous resources.

scholarly journals A single gas chromatograph for accurate atmospheric mixing ratio measurements of CO<sub>2</sub>, CH<sub>4</sub>, N<sub>2</sub>O, SF<sub>6</sub> and CO

2009 ◽  
Vol 2 (2) ◽  
pp. 549-559 ◽  
Author(s):  
S. van der Laan ◽  
R. E. M. Neubert ◽  
H. A. J. Meijer
Keyword(s):  
Research Station ◽  
Time Lags ◽  
Gas Chromatograph ◽  
Efficient System ◽  
Term Stability ◽  
Mixing Ratios ◽  
Long Term Stability ◽  
High Measurement ◽  
Cost Efficient

Abstract. We present an adapted gas chromatograph capable of measuring simultaneously and semi-continuously the atmospheric mixing ratios of the greenhouse gases CO2, CH4, N2O and SF6 and the trace gas CO with high precision and long-term stability. The novelty of our design is that all species are measured with only one device, making it a very cost-efficient system. No time lags are introduced between the measured mixing ratios. The system is designed to operate fully autonomously which makes it ideal for measurements at remote and unmanned stations. Only a small amount of sample air is needed, which makes this system also highly suitable for flask air measurements. In principle, only two reference cylinders are needed for daily operation and only one calibration per year against international WMO standards is sufficient to obtain high measurement precision and accuracy. The system described in this paper is in use since May 2006 at our atmospheric measurement site Lutjewad near Groningen, The Netherlands at 6°21´ E, 53°24´N, 1 m a.s.l. Results show the long-term stability of the system. Observed measurement precisions at our remote research station Lutjewad were: ±0.04 ppm for CO2, ±0.8 ppb for CH4, ±0.8 ppb for CO, ±0.3 ppb for N2O, and ±0.1 ppt for SF6. The ambient mixing ratios of all measured species as observed at station Lutjewad for the period of May 2007 to August 2008 are presented as well.

Real-Time Marine Diesel Engine Simulation for Fault Diagnosis

2002 ◽  
Vol 39 (01) ◽  
pp. 21-28
Author(s):  
Kevin Logan ◽  
Bahadir Inozu ◽  
Philippe Roy ◽  
Jean-Francçois Hetet ◽  
Pascal Chesse ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Real Time ◽  
Learning System ◽  
Cerebellar Model Articulation Controller ◽  
Diagnostic Systems ◽  
Efficient Manner ◽  
Engine Simulation ◽  
Intelligent Software ◽  
Marine Diesel ◽  
Cost Efficient

Automated monitoring systems are now the standard on most large vessels; however, few are equipped with diagnostic systems. This paper presents new developments in the area of fault diagnosis based on intelligent software agents. The research objective was to design an agent capable of continuous real-time machine learning by using an artificial neural network known as the cerebellar model articulation controller (CMAC). An engine simulator that can model both normal and faulty engine operations was used to develop the learning system controller in a flexible and cost-efficient manner. This paper provides a description of the selected CMAC, a brief overview of the real-time engine simulator and its integration with the learning system as well as a few results.

Cost efficient resource allocation for real-time tasks in embedded systems

2019 ◽  
Vol 48 ◽  
pp. 101523 ◽  
Author(s):  
Nasro Min-Allah ◽  
Muhammad Bilal Qureshi ◽  
Saleh Alrashed ◽  
Omer F. Rana
Keyword(s):  
Resource Allocation ◽  
Embedded Systems ◽  
Real Time ◽  
Efficient Resource ◽  
Cost Efficient
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