A novel vibration based breathing crack localization technique using a single sensor measurement

2019 ◽  
Vol 122 ◽  
pp. 117-138 ◽  
Author(s):  
J. Prawin ◽  
K. Lakshmi ◽  
A. Rama Mohan Rao
Keyword(s):  
Breathing Crack ◽  
Localization Technique ◽  
Single Sensor ◽  
Sensor Measurement

scholarly journals Measuring the Daylight Performance of Classrooms: Can a One Point Sensor Measurement Predict the Daylight Distribution within a Space?

2020 ◽  
Author(s):  
Aniebietabasi Ackley ◽  
Michael Donn ◽  
geoff Thomas
Keyword(s):  
Vertical Wall ◽  
School Buildings ◽  
Ministry Of Education ◽  
Routine Measurement ◽  
Single Sensor ◽  
Study Support ◽  
Co2 Level ◽  
Sensor Measurement ◽  
Daylight Performance ◽  
Temperature Noise

The New Zealand Ministry of Education (MoE) has begun measuring the light, temperature, noise and CO2 level of 21 selected schools using a single sensor. This sensor is being developed as a method for routine measurement in order to understand the performance of New Zealand's school buildings. This study used a Climate Based Daylight Modelling to appraise the MoE methodology, to determine what can be learned from the use of a single sensor in one location in a classroom, to estimate the lighting comfort across a space. Daylighting is focused upon because it has the most spatial variation in a space. The findings of this study support the assertion that a one-point sensor measurement on a vertical wall could predict illuminance across the centre of the horizontal work plane; and provide a useful benchmark to estimate the light distribution across a space. However, regardless of how representative of a space a one-point measurement is, it is difficult to quantify the daylight distribution over time throughout the space. If various daylight indicators are well documented and analysed alongside the measured data, a strategically positioned one-point sensor on the vertical wall could be useful in predicting the daylight quantity of a space.

scholarly journals Measuring the Daylight Performance of Classrooms: Can a One Point Sensor Measurement Predict the Daylight Distribution within a Space?

2020 ◽  
Author(s):  
Aniebietabasi Ackley ◽  
Michael Donn ◽  
geoff Thomas
Keyword(s):  
Vertical Wall ◽  
School Buildings ◽  
Ministry Of Education ◽  
Routine Measurement ◽  
Single Sensor ◽  
Study Support ◽  
Co2 Level ◽  
Sensor Measurement ◽  
Daylight Performance ◽  
Temperature Noise

The New Zealand Ministry of Education (MoE) has begun measuring the light, temperature, noise and CO2 level of 21 selected schools using a single sensor. This sensor is being developed as a method for routine measurement in order to understand the performance of New Zealand's school buildings. This study used a Climate Based Daylight Modelling to appraise the MoE methodology, to determine what can be learned from the use of a single sensor in one location in a classroom, to estimate the lighting comfort across a space. Daylighting is focused upon because it has the most spatial variation in a space. The findings of this study support the assertion that a one-point sensor measurement on a vertical wall could predict illuminance across the centre of the horizontal work plane; and provide a useful benchmark to estimate the light distribution across a space. However, regardless of how representative of a space a one-point measurement is, it is difficult to quantify the daylight distribution over time throughout the space. If various daylight indicators are well documented and analysed alongside the measured data, a strategically positioned one-point sensor on the vertical wall could be useful in predicting the daylight quantity of a space.

A practical approach on non-regular sampling and universal demosaicing of raw image sensor data

2020 ◽  
Vol 2020 (1) ◽  
pp. 91-95
Author(s):  
Philipp Backes ◽  
Jan Fröhlich
Keyword(s):  
Image Quality ◽  
Source Code ◽  
Image Sensor ◽  
Sensor Data ◽  
Color Filter ◽  
Lowpass Filter ◽  
Sampling Errors ◽  
Regular Sampling ◽  
Single Sensor ◽  
Similar Image Quality

Non-regular sampling is a well-known method to avoid aliasing in digital images. However, the vast majority of single sensor cameras use regular organized color filter arrays (CFAs), that require an optical-lowpass filter (OLPF) and sophisticated demosaicing algorithms to suppress sampling errors. In this paper a variety of non-regular sampling patterns are evaluated, and a new universal demosaicing algorithm based on the frequency selective reconstruction is presented. By simulating such sensors it is shown that images acquired with non-regular CFAs and no OLPF can lead to a similar image quality compared to their filtered and regular sampled counterparts. The MATLAB source code and results are available at: http://github. com/PhilippBackes/dFSR

dC/dV and CV Characterization of Gate Resistance Defects in eDRAM Circuits

2013 ◽  
Author(s):  
Sweta Pendyala ◽  
Dave Albert ◽  
Katherine Hawkins ◽  
Michael Tenney
Keyword(s):  
Deep Submicron ◽  
Work Flow ◽  
Localization Technique ◽  
Characterization Techniques ◽  
Capacitance Voltage ◽  
Gate Resistance

Abstract Resistive gate defects are unusual and difficult to detect with conventional techniques [1] especially on advanced devices manufactured with deep submicron SOI technologies. An advanced localization technique such as Scanning Capacitance Imaging is essential for localizing these defects, which can be followed by DC probing, dC/dV, CV (Capacitance-Voltage) measurements to completely characterize the defect. This paper presents a case study demonstrating this work flow of characterization techniques.

FINAL VERSION.pdf

2019 ◽  
Author(s):  
Nasir Saeed ◽  
Mohamed-Slim Alouini ◽  
Tareq Y. Al-Naffouri
Keyword(s):  
Graph Partitioning ◽  
Three Dimensional ◽  
Smart Objects ◽  
Localization Technique ◽  
Optical Internet ◽  
Spectral Graph ◽  
Accurate Localization ◽  
Spectral Graph Partitioning ◽  
Link Connectivity

<div>Localization is a fundamental task for optical internet</div><div>of underwater things (O-IoUT) to enable various applications</div><div>such as data tagging, routing, navigation, and maintaining link connectivity. The accuracy of the localization techniques for OIoUT greatly relies on the location of the anchors. Therefore, recently localization techniques for O-IoUT which optimize the anchor’s location are proposed. However, optimization of anchors location for all the smart objects in the network is not a useful solution. Indeed, in a network of densely populated smart objects, the data collected by some sensors are more valuable than the data collected from other sensors. Therefore, in this paper, we propose a three-dimensional accurate localization technique by optimizing the anchor’s location for a set of smart objects. Spectral graph partitioning is used to select the set of valuable</div><div>sensors.</div>

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