Automated Noise Calibration System (VT-1000)

NCSLI Measure ◽  
2021 ◽  
Vol 13 (3) ◽  
pp. 17-33
Author(s):  
Iraj Visali ◽  
Scott Timpe
Keyword(s):  
Uncertainty Quantification ◽  
Real Time ◽  
Noise Source ◽  
Noise Figure ◽  
Automated System ◽  
Excess Noise ◽  
Calibration System ◽  
Report Generation ◽  
California Institute Of Technology ◽  
Detailed Instruction

This paper details an automated Noise Source calibration system in development at Jet Propulsion Laboratory, California Institute of Technology (JPL). The paper begins with a discussion on noise figure and excess-noise-ratio (ENR) theory, fundamentals and governing equations. As part of the fundamentals there is a discussion of the system’s use of the Y-factor method to obtain accurate measurements of the unit under test (UUT), and how these measurements are compared against a known ENR standard to obtain the UUT’s ENR values. There is also an in-depth discussion on uncertainty quantification for noise source system calibrations. The architecture of the automated calibration system is provided, which includes both the system’s hardware and software configuration. The software is written in Python 3, and provides the user detailed instruction on how to proceed, including step-by-step connection requirements. This system automates much of the measurement process, including real-time uncertainty quantification and report generation, as well as real-time feedback to the user to allow intervention. The system takes advantage of a database of results from previous measurements to compare calibration history of the ENR measurements. The automated system presented here operates over a frequency range from 10 MHz to 50 GHz, and has shown substantial time savings over traditional manual methods of performing this calibration

scholarly journals Decreased noise figure measurement uncertainty in <i>Y</i> factor method

2013 ◽  
Vol 11 ◽  
pp. 1-5
Author(s):  
I. Gaspard
Keyword(s):  
Measurement Uncertainty ◽  
Noise Source ◽  
Noise Figure ◽  
Cold Temperature ◽  
Excess Noise ◽  
Noise Power ◽  
Noise Sources ◽  
Hot Temperature ◽  
Factor Method ◽  
Equivalent Noise Power

Abstract. Almost always noise figure is measured today by using a matched noise source delivering two different but known noise temperatures (Y factor method). In commercially available noise sources these temperatures are characterized by the excess noise ratio (ENR) value, describing the ratio of equivalent noise power when switched on related to the noise power of a resistor at a temperature of 290 K (switched off). For a typical ENR value of 5 dB that means a hot temperature Th=1207 K and a cold temperature Tc = 290 K.

Smart Power Supply Calibration System

2021 ◽  
Vol 13 (1) ◽  
pp. 22-27
Author(s):  
Iraj Vasaeli ◽  
Brandon Umansky
Keyword(s):  
Power Supply ◽  
Power Supplies ◽  
Automated System ◽  
California Institute ◽  
Calibration System ◽  
Smart Power ◽  
California Institute Of Technology ◽  
Custom Software ◽  
Time Savings ◽  
Institute Of Technology

This paper details the development of an automated procedure to conduct calibrations of power supplies at Jet Propulsion Laboratory, California Institute of Technology (JPL). The fundamentals of power supply calibrations are given, and discussion on the method by which this custom software handles that calibration. Additionally, this technique provides real time uncertainty quantification of the calibrations. This automated system has demonstrated a time savings over existing automated techniques in use today.

Near-Perfect Automation: Investigating Performance, Trust, and Visual Attention Allocation

2021 ◽  
pp. 001872082110328
Author(s):  
Cyrus K. Foroughi ◽  
Shannon Devlin ◽  
Richard Pak ◽  
Noelle L. Brown ◽  
Ciara Sibley ◽  
...  
Keyword(s):  
Visual Attention ◽  
False Alarm ◽  
Real Time ◽  
Subjective Experience ◽  
Automated System ◽  
Automated Systems ◽  
High Stakes ◽  
Individual Level ◽  
Scan Pattern ◽  
Subjective Trust

Objective Assess performance, trust, and visual attention during the monitoring of a near-perfect automated system. Background Research rarely attempts to assess performance, trust, and visual attention in near-perfect automated systems even though they will be relied on in high-stakes environments. Methods Seventy-three participants completed a 40-min supervisory control task where they monitored three search feeds. All search feeds were 100% reliable with the exception of two automation failures: one miss and one false alarm. Eye-tracking and subjective trust data were collected. Results Thirty-four percent of participants correctly identified the automation miss, and 67% correctly identified the automation false alarm. Subjective trust increased when participants did not detect the automation failures and decreased when they did. Participants who detected the false alarm had a more complex scan pattern in the 2 min centered around the automation failure compared with those who did not. Additionally, those who detected the failures had longer dwell times in and transitioned to the center sensor feed significantly more often. Conclusion Not only does this work highlight the limitations of the human when monitoring near-perfect automated systems, it begins to quantify the subjective experience and attentional cost of the human. It further emphasizes the need to (1) reevaluate the role of the operator in future high-stakes environments and (2) understand the human on an individual level and actively design for the given individual when working with near-perfect automated systems. Application Multiple operator-level measures should be collected in real-time in order to monitor an operator’s state and leverage real-time, individualized assistance.

scholarly journals NIAGADS Alzheimer’s GenomicsDB: A resource for exploring Alzheimer’s Disease genetic and genomic knowledge

2020 ◽  
Author(s):  
Emily Greenfest-Allen ◽  
Conor Klamann ◽  
Prabhakaran Gangadharan ◽  
Amanda Kuzma ◽  
Yuk Yee Leung ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Real Time ◽  
Data Repository ◽  
Sequencing Analysis ◽  
Summary Statistics ◽  
Time Data ◽  
Report Generation ◽  
Sequencing Project ◽  
Gene Annotations

AbstractINTRODUCTIONThe NIAGADS Alzheimer’s Genomics Database is an interactive knowledgebase for AD genetics that provides access to GWAS summary statistics datasets deposited at NIAGADS, a national genetics data repository for AD and related dementia (ADRD).METHODSThe website makes available >70 genome-wide summary statistics datasets from GWAS and genome sequencing analysis for AD/ADRD. Variants identified from these datasets are mapped to up-to-date variant and gene annotations from a variety of resources and linked to functional genomics data.The database is powered by a big data optimized relational database and ontologies to consistently annotate study designs and phenotypes, facilitating data harmonization and efficient real-time data analysis and variant or gene report generation.RESULTSDetailed variant reports provide tabular and interactive graphical summaries of known ADRD associations, as well as highlight variants flagged by the Alzheimer’s Disease Sequencing Project (ADSP). Gene reports provide summaries of co-located ADRD risk-associated variants and have been expanded to include meta-analysis results from aggregate association tests performed by the ADSP allowing us to flag genes with genetic-evidence for AD.DISCUSSIONThe GenomicsDB makes available >100 million variant annotations, including ~30 million (5 million novel) variants identified as AD-relevant by ADSP, for browsing and real-time mining via the website or programmatically through a REST API. With a newly redesigned, efficient, search interface and comprehensive record pages linking summary statistics to variant and gene annotations, this resource makes these data both accessible and interpretable, establishing itself as valuable tool for AD research.

scholarly journals TreeNet–The Biological Drought and Growth Indicator Network

2021 ◽  
Vol 4 ◽  
Author(s):  
Roman Zweifel ◽  
Sophia Etzold ◽  
David Basler ◽  
Reinhard Bischoff ◽  
Sabine Braun ◽  
...  
Keyword(s):  
Data Processing ◽  
Real Time ◽  
Environmental Conditions ◽  
Forest Growth ◽  
Water Dynamics ◽  
Automated System ◽  
Indicator Network ◽  
High Temporal Resolution ◽  
Stem Size ◽  
Wide Range

The TreeNet research and monitoring network has been continuously collecting data from point dendrometers and air and soil microclimate using an automated system since 2011. The goal of TreeNet is to generate high temporal resolution datasets of tree growth and tree water dynamics for research and to provide near real-time indicators of forest growth performance and drought stress to a wide audience. This paper explains the key working steps from the installation of sensors in the field to data acquisition, data transmission, data processing, and online visualization. Moreover, we discuss the underlying premises to convert dynamic stem size changes into relevant biological information. Every 10 min, the stem radii of about 420 trees from 13 species at 61 sites in Switzerland are measured electronically with micrometer precision, in parallel with the environmental conditions above and below ground. The data are automatically transmitted, processed and stored on a central server. Automated data processing (R-based functions) includes screening of outliers, interpolation of data gaps, and extraction of radial stem growth and water deficit for each tree. These long-term data are used for scientific investigations as well as to calculate and display daily indicators of growth trends and drought levels in Switzerland based on historical and current data. The current collection of over 100 million data points forms the basis for identifying dynamics of tree-, site- and species-specific processes along environmental gradients. TreeNet is one of the few forest networks capable of tracking the diurnal and seasonal cycles of tree physiology in near real-time, covering a wide range of temperate forest species and their respective environmental conditions.

Development of an Intelligent Automated System for Dialogue and Decision-Making in Real Time

2018 ◽  
Author(s):  
Andrey Tarasiev ◽  
Egor Talancev ◽  
Konstantin Aksyonov ◽  
Uriy Chiryshev ◽  
Olga Aksyonova ◽  
...  
Keyword(s):  
Decision Making ◽  
Real Time ◽  
Automated System

scholarly journals A New Online Dynamic Testing Method for Noise of Broadcast Transmitter

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Shan-Shan Li ◽  
Jian Zhou ◽  
Xuan Wang
Keyword(s):  
Real Time ◽  
Data Storage ◽  
Dynamic Testing ◽  
Noise Figure ◽  
Estimation Algorithm ◽  
Test Method ◽  
Noise Estimation ◽  
Experimental Simulation ◽  
Spectrum Estimation ◽  
Low Efficiency

Aiming at the shortcomings of traditional broadcast transmitter noise test methods, such as low efficiency, inconvenient data storage, and high requirements for testers, a dynamic online test method for transmitter noise is proposed. The principle of system composition and test method is given. The transmitter noise is real-time changing. The Voice Active Detection (VAD) noise estimation algorithm cannot track the transmitter noise change in real time. This paper proposes a combined noise estimation algorithm for VAD and dynamic estimation. By setting the threshold of the double-threshold VAD detection to be low, it can accurately detect the silent segment. The silent segment is used as a noise signal for noise estimation. For the nonsilent segment detected by the VAD, a minimum value search dynamic spectrum estimation algorithm based on the existence probability of the speech (IMCRA) is used for noise estimation. Transmitter noise is measured by calculating the noise figure (NF).The test method collects the input and output data of the transmitter in real time, which has better accuracy and real-time performance, and the feasibility of the method is verified by experimental simulation.

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