Thermal and Optical Enhancements to Liquid Crystal Hot Spot Detection Methods

1997 ◽  
Author(s):  
S. Ferrier
Keyword(s):  
Liquid Crystal ◽  
Hot Spots ◽  
Hot Spot ◽  
Low Cost ◽  
Thermal Modeling ◽  
Detection Methods ◽  
Optical Sensitivity ◽  
Spot Detection

Abstract Three enhancements to Liquid Crystal hot spot detection improve thermal and optical sensitivity while substantially maintaining simplicity, safety and relative low cost. These enhancements have permitted detection of hot spots unidentifiable by traditional LC methods. Details, capabilities and limitations of the enhancements are discussed, results of rudimentary defect thermal modeling are presented, and an improved metric for evaluating LC technique sensitivity is proposed.

Backside Hot Spot Detection Using Liquid Crystal Microscopy

2002 ◽  
Vol 42 (9-11) ◽  
pp. 1741-1746 ◽  
Author(s):  
O. Crepel ◽  
F. Beaudoin ◽  
L. Dantas de Morais ◽  
G. Haller ◽  
C, Goupil ◽  
...  
Keyword(s):  
Liquid Crystal ◽  
Hot Spot ◽  
Spot Detection

Solvent Accessible Surface Area-Based Hot-Spot Detection Methods for Protein–Protein and Protein–Nucleic Acid Interfaces

2015 ◽  
Vol 55 (5) ◽  
pp. 1077-1086 ◽  
Author(s):  
Cristian R. Munteanu ◽  
António C. Pimenta ◽  
Carlos Fernandez-Lozano ◽  
André Melo ◽  
Maria N. D. S. Cordeiro ◽  
...  
Keyword(s):  
Nucleic Acid ◽  
Surface Area ◽  
Hot Spot ◽  
Accessible Surface Area ◽  
Detection Methods ◽  
Solvent Accessible Surface Area ◽  
Spot Detection ◽  
Protein Nucleic Acid ◽  
Accessible Surface ◽  
Solvent Accessible Surface

Improving Earth hot-spot detection from MODIS data using MODVOLC algorithm

2020 ◽  
Author(s):  
Andrea Gabrieli ◽  
Robert Wright ◽  
Harold Garbeil ◽  
Eric Pilger
Keyword(s):  
Hot Spots ◽  
Thermal Emission ◽  
Hot Spot ◽  
Image Data ◽  
Anthropogenic Heat ◽  
Heat Sources ◽  
Lower Intensity ◽  
New Approach ◽  
Spot Detection ◽  
Modis Image

<p>Space-borne hot-spot detection on the Earth surface is key to monitoring and studying volcanic activity, wildfires and anthropogenic heat sources from space. Lower intensity thermal emission hot-spots, which often represent the onset of volcanic eruptions and large wildfires, are difficult to detect. We are improving the MODVOLC algorithm, which monitors Earth’s surface for hot-spots by analyzing Moderate Resolution Imaging Spectroradiometer (MODIS) data every 48 hours, to allow lower intensity thermal emission detection. Improving the existing MODVOLC algorithm for hot-spot detection from MODIS image data is not trivial. A new approach, which we refer it to as the Maximum Radiance Algorithm for MODIS, has been explored. The new approach requires a MODIS 4 µm and accompanying 12 µm global radiance time-series at ~1 km grid spacing. This reference data set describes the maximum radiance that has been measured from each square km of Earth’s surface over a ten year period (having first excluded high natural and anthropogenic heat sources from the time-series, using the existing MODVOLC approach). For each new geolocated MODIS image data, the observed radiance for each pixel is compared with this reference, and if its radiance exceeds the historical maximum, it can be considered a potential hot-spot. A dynamic tolerance is used to then confirm if the potential hot-spot is an actual hot-spot. We show that this new approach for hot-spot detection offers significant advantage over existing techniques for lower intensity thermal emission hot-spot detection during both day and nighttime conditions.</p>

scholarly journals RSTVOLC implementation on MODIS data for monitoring of thermal volcanic activity

2011 ◽  
Vol 54 (5) ◽  
Author(s):  
Teodosio Lacava ◽  
Francesco Marchese ◽  
Nicola Pergola ◽  
Valerio Tramutoli ◽  
Irina Coviello ◽  
...  
Keyword(s):  
Volcanic Activity ◽  
Hot Spots ◽  
Hot Spot ◽  
Mount Etna ◽  
Satellite Monitoring ◽  
Modis Data ◽  
Spot Detection ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Moderate Resolution Imaging Spectroradiometer

An optimized configuration of the Robust Satellite Technique (RST) approach was developed within the framework of the ‘LAVA’ project. This project is funded by the Italian Department of Civil Protection and the Italian Istituto Nazionale di Geofisica e Vulcanologia, with the aim to improve the effectiveness of satellite monitoring of thermal volcanic activity. This improved RST configuration, named RSTVOLC, has recently been implemented in an automatic processing chain that was developed to detect hot-spots in near real-time for Italian volcanoes. This study presents the results obtained for the Mount Etna eruption of July 14-24, 2006, using the Moderate Resolution Imaging Spectroradiometer (MODIS) data. To better assess the operational performance, the RSTVOLC results are also discussed in comparison with those obtained by MODVOLC, a well-established, MODIS-based algorithm for hot-spot detection that is used worldwide.

scholarly journals Potential Application of Hough Transform to Extract Hot Spot Delineated Boundaries using Landsat-8 Satellite Images

2020 ◽  
Vol 9 (3) ◽  
pp. 3550-3557
Keyword(s):  
Hough Transform ◽  
Hot Spots ◽  
Large Scale ◽  
Hot Spot ◽  
Satellite Image ◽  
Detection Algorithm ◽  
Landsat 8 ◽  
Spot Detection ◽  
Canny Edge ◽  
Satellite Image Analysis

Coal fires, also known as subsurface fires or hot spots are all-inclusive issues in coal mines everywhere throughout the globe. Aimless mining over a period of past 100 years has prompted large scale damages to the ecosystem of the earth. For example, debasement in nature of water, soil, air, vegetation dissemination and variations in land topography have caused degradation. Research is needed to be more attentive on developing the prospective use of the satellite image analysis for hot spot detection because ground-based hot spots monitoring is time-taking, complex, cumbrous and very expensive. In this paper, a two-stage model has been developed to extract the hot spot delineated boundaries in Jharia coal field (JCF) region. In the first stage, contextual thresholding (CT) technique has been used to classify the hot spot and non-hot spot regions. After thorough processing, hot spots regions have been retrieved and for performance evaluation sensitivity and specificity are calculated, which suggest that hot spots were detected accurately in successful and efficient way. In second stage, the Canny edge detection algorithm is applied to detect the edges of the hot spot regions and then the binary image is generated, which is later converted into a vector image. Finally Hough transform is implemented on the obtained vector images for delineating hot spot boundaries. In future, delineated hot spot boundaries may be used to obtain the expansion or shrinking information of hot spot regions and it can be used for area estimation also.

Infrared Thermography Developments for III-V Transistors and MMICs

2011 ◽  
Author(s):  
Dominique Carisetti ◽  
Mohsine Bouya ◽  
Odile Bezencenet ◽  
Bernard Servet ◽  
Jean-Claude Clément ◽  
...  
Keyword(s):  
Liquid Crystal ◽  
Failure Analysis ◽  
Infrared Thermography ◽  
Hot Spot ◽  
Ir Thermography ◽  
Thermal Measurement ◽  
Metal Insulator ◽  
Spot Detection ◽  
Metal Insulator Metal ◽  
Thermal Measurements

Abstract This paper focuses on infrared (IR) thermography capabilities on III-V components for thermal measurements applications and failure analysis (FA). The first part discusses the thermal mapping on InGaAs/AlGaAs PHEMT structure and compares IR thermal measurement with the well-known techniques as Raman and SThM. The second part discusses IR thermography on challenging FA for hot spot detection on the most popular type of capacitor for III-V MMICs as the metal-insulator-metal capacitor. It shows how IR thermography can easily localize very small pinholes in SiN, where liquid crystal and OBIRCH techniques are not well adapted.

TPLY for Yield Improvement

1996 ◽  
Author(s):  
G. Matusiewicz
Keyword(s):  
Liquid Crystal ◽  
Electron Microscope ◽  
Statistical Methods ◽  
Hot Spot ◽  
Test Site ◽  
Final Test ◽  
Yield Model ◽  
Yield Improvement ◽  
Root Cause ◽  
Spot Detection

Abstract A strategy for improving yield lately is this: wafers are electrically tested to determine which chips are defective. Defects are located and classified by the failure analyst who establishes a root cause for each type of defect. This information is delivered to the process engineers who then eliminate the root causes of the most frequently occurring defects. This process is known as TPLY. for Tested Product Limited Yield. This method gives the physical failure analyst the job of finding statistical numbers of defects. Defects are located by deprocessing chips and examining them with an optical or electron microscope, usually in an area of the chip identified by a bit fail map or some other technique, such as liquid crystal hot spot detection. This presentation offers practical suggestions for improving the efficiency of the TPLY process. It includes general considerations for TPLY, methods for delayering chips and finding defects quickly, and statistical methods for identifying the cause of low yield with minimum sample sizes. A simple yield model is developed for relating test site yield to product chip final test yield, and explains why test sites do not always adequately predict yield of the product. Case studies and other examples are discussed to demonstrate the application of these techniques.

scholarly journals Issues in the Current Practices of Spatial Cluster Detection and Exploring Alternative Methods

2021 ◽  
Vol 18 (18) ◽  
pp. 9848
Author(s):  
David Wong
Keyword(s):  
Extreme Values ◽  
Hot Spot ◽  
Heuristic Method ◽  
Cluster Detection ◽  
Alternative Methods ◽  
Detection Methods ◽  
Cold Spot ◽  
Spot Detection ◽  
Cold Spots ◽  
Local Moran

Local Moran and local G-statistic are commonly used to identify high-value (hot spot) and low-value (cold spot) spatial clusters for various purposes. However, these popular tools are based on the concept of spatial autocorrelation or association (SA), but do not explicitly consider if values are high or low enough to deserve attention. Resultant clusters may not include areas with extreme values that practitioners often want to identify when using these tools. Additionally, these tools are based on statistics that assume observed values or estimates are highly accurate with error levels that can be ignored or are spatially uniform. In this article, problems associated with these popular SA-based cluster detection tools were illustrated. Alternative hot spot-cold spot detection methods considering estimate error were explored. The class separability classification method was demonstrated to produce useful results. A heuristic hot spot-cold spot identification method was also proposed. Based on user-determined threshold values, areas with estimates exceeding the thresholds were treated as seeds. These seeds and neighboring areas with estimates that were not statistically different from those in the seeds at a given confidence level constituted the hot spots and cold spots. Results from the heuristic method were intuitively meaningful and practically valuable.

scholarly journals MegaSense: Feasibility of Low-Cost Sensors for Pollution Hot-spot Detection

2019 ◽  
Author(s):  
Eemil Lagerspetz ◽  
Sasu Tarkoma ◽  
Tareq Hussein ◽  
Naser Hossein Motlagh ◽  
Martha Arbayani Zaidan ◽  
...  
Keyword(s):  
Hot Spot ◽  
Low Cost ◽  
Spot Detection
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