Extending “Deep Blue” aerosol retrieval coverage to cases of absorbing aerosols above clouds: Sensitivity analysis and first case studies

Abstract Laser-based dynamic analysis has become a very important tool for analyzing advanced process technology and complex circuit design. Thus, many good reference papers discuss high resolution, high sensitivity, and useful applications. However, proper interpretation of the measurement is important as well to understand the failure behavior and find the root cause. This paper demonstrates this importance by describing two insightful case studies with unique observations from laser voltage imaging/laser voltage probing (LVP), optical beam induced resistance change, and soft defect localization (SDL) analysis, which required an in-depth interpretation of the failure analysis (FA) results. The first case is a sawtooth LVP signal induced by a metal short. The second case, a mismatched result between an LVP and SDL analysis, is a good case of unusual LVP data induced by a very sensitive response to laser light. The two cases provide a good reference on how to properly explain FA results.

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A Silicon Debugging Methodology on Customer Prototype for Wafer Foundries

ISTFA 2012: Conference Proceedings from the 38th International Symposium for Testing and Failure Analysis ◽

10.31399/asm.cp.istfa2012p0411 ◽

2012 ◽

Author(s):

Mai Zhihong ◽

Ng Tsu Hau ◽

Dawood M. Khalid ◽

Tan Pik Kee ◽

Jeffrey Lam

Keyword(s):

Case Studies ◽

Semiconductor Industry ◽

Full Information ◽

Limited Information ◽

Ip Protection ◽

First Case ◽

Silicon Debugging ◽

Failure Location ◽

Device Chip ◽

Latch Up

Abstract IP protection is of major importance for a semiconductor company and only limited information is made available for device debugging for the product outsourced to a foundry. In order to position ourselves better in the ever competitive semiconductor industry, with the consideration of IP protection, we have to provide the customers with the Si debugging capability and device/chip verification services in foundry. This paper explores the Si debugging methodology and technique in a foundry. Two case studies are presented and discussed. The first case illustrates the isolation of the failure location by InGaAs microscopy, upon which the failure was identified to be caused by a latch-up issue. In the second case, due to confidentiality considerations from the customer, full information could not be provided to the foundry for silicon debugging. The paper illustrates the ability to effectively debug a failure despite being constrained by limited information from the customer.

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Developing Objective Sensitivity Analysis of Periodic Systems: Case Studies of Biological Oscillators

ACTA AUTOMATICA SINICA ◽

10.3724/sp.j.1004.2012.01065 ◽

2012 ◽

Vol 38 (7) ◽

pp. 1065 ◽

Cited By ~ 1

Author(s):

Bao-Yun LU ◽

Hong YUE

Keyword(s):

Sensitivity Analysis ◽

Case Studies ◽

Periodic Systems ◽

Biological Oscillators

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Investigating the potential of redundant manipulators in narrow channels

Proceedings of the Institution of Mechanical Engineers Part C Journal of Mechanical Engineering Science ◽

10.1177/0954406220964512 ◽

2020 ◽

pp. 095440622096451

Author(s):

Ashish Singla ◽

Jyotindra Narayan ◽

Himanshu Arora

Keyword(s):

Case Studies ◽

Fuzzy Inference ◽

Redundant Manipulators ◽

Redundancy Resolution ◽

Inference System ◽

Narrow Channels ◽

First Case ◽

Task Priority ◽

Patient Side

In this paper, an attempt has been made to investigate the potential of redundant manipulators, while tracking trajectories in narrow channels. The behavior of redundant manipulators is important in many challenging applications like under-water welding in narrow tanks, checking the blockage in sewerage pipes, performing a laparoscopy operation etc. To demonstrate this snake-like behavior, redundancy resolution scheme is utilized using two different approaches. The first approach is based on the concept of task priority, where a given task is split and prioritize into several subtasks like singularity avoidance, obstacle avoidance, torque minimization, and position preference over orientation etc. The second approach is based on Adaptive Neuro Fuzzy Inference System (ANFIS), where the training is provided through given datasets and the results are back-propagated using augmentation of neural networks with fuzzy logics. Three case studies are considered in this work to demonstrate the redundancy resolution of serial manipulators. The first case study of 3-link manipulator is attempted with both the approaches, where the objective is to track the desired trajectory while avoiding multiple obstacles. The second case study of 7-link manipulator, tracking trajectory in a narrow channel, is investigated using the concept of task priority. The realistic application of minimum-invasive surgery (MIS) based trajectory tracking is considered as the third case study, which is attempted using ANFIS approach. The 5-link spatial redundant manipulator, also known as a patient-side manipulator being developed at CSIR-CSIO, Chandigarh is used to track the desired surgical cuts. Through the three case studies, it is well demonstrated that both the approaches are giving satisfactory results.

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Sensitivity analysis for interpretation of machine learning based segmentation models in cardiac MRI

BMC Medical Imaging ◽

10.1186/s12880-021-00551-1 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Markus J. Ankenbrand ◽

Liliia Shainberg ◽

Michael Hock ◽

David Lohr ◽

Laura M. Schreiber

Keyword(s):

Neural Networks ◽

Sensitivity Analysis ◽

Cardiac Magnetic Resonance ◽

Magnetic Resonance ◽

Cardiac Mri ◽

Magnetic Resonance Images ◽

Model Interpretation ◽

First Case ◽

Segmentation Models

Abstract Background Image segmentation is a common task in medical imaging e.g., for volumetry analysis in cardiac MRI. Artificial neural networks are used to automate this task with performance similar to manual operators. However, this performance is only achieved in the narrow tasks networks are trained on. Performance drops dramatically when data characteristics differ from the training set properties. Moreover, neural networks are commonly considered black boxes, because it is hard to understand how they make decisions and why they fail. Therefore, it is also hard to predict whether they will generalize and work well with new data. Here we present a generic method for segmentation model interpretation. Sensitivity analysis is an approach where model input is modified in a controlled manner and the effect of these modifications on the model output is evaluated. This method yields insights into the sensitivity of the model to these alterations and therefore to the importance of certain features on segmentation performance. Results We present an open-source Python library (misas), that facilitates the use of sensitivity analysis with arbitrary data and models. We show that this method is a suitable approach to answer practical questions regarding use and functionality of segmentation models. We demonstrate this in two case studies on cardiac magnetic resonance imaging. The first case study explores the suitability of a published network for use on a public dataset the network has not been trained on. The second case study demonstrates how sensitivity analysis can be used to evaluate the robustness of a newly trained model. Conclusions Sensitivity analysis is a useful tool for deep learning developers as well as users such as clinicians. It extends their toolbox, enabling and improving interpretability of segmentation models. Enhancing our understanding of neural networks through sensitivity analysis also assists in decision making. Although demonstrated only on cardiac magnetic resonance images this approach and software are much more broadly applicable.

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Assessing Environmental Factors within the One Health Approach

Medicina ◽

10.3390/medicina57030240 ◽

2021 ◽

Vol 57 (3) ◽

pp. 240

Author(s):

Sarah Humboldt-Dachroeden ◽

Alberto Mantovani

Keyword(s):

Case Studies ◽

One Health ◽

Animal Health ◽

Grey Literature ◽

Aflatoxin M1 ◽

First Case ◽

Italian Regions ◽

Environmental Component ◽

Environmental Considerations ◽

The One

Background: One Health is a comprehensive and multisectoral approach to assess and examine the health of animals, humans and the environment. However, while the One Health approach gains increasing momentum, its practical application meets hindrances. This paper investigates the environmental pillar of the One Health approach, using two case studies to highlight the integration of environmental considerations. The first case study pertains to the Danish monitoring and surveillance programme for antimicrobial resistance, DANMAP. The second case illustrates the occurrence of aflatoxin M1 (AFM1) in milk in dairy-producing ruminants in Italian regions. Method: A scientific literature search was conducted in PubMed and Web of Science to locate articles informing the two cases. Grey literature was gathered to describe the cases as well as their contexts. Results: 19 articles and 10 reports were reviewed and informed the two cases. The cases show how the environmental component influences the apparent impacts for human and animal health. The DANMAP highlights the two approaches One Health and farm to fork. The literature provides information on the comprehensiveness of the DANMAP, but highlights some shortcomings in terms of environmental considerations. The AFM1 case, the milk metabolite of the carcinogenic mycotoxin aflatoxin B1, shows that dairy products are heavily impacted by changes of the climate as well as by economic drivers. Conclusions: The two cases show that environmental conditions directly influence the onset and diffusion of hazardous factors. Climate change, treatment of soils, water and standards in slaughterhouses as well as farms can have a great impact on the health of animals, humans and the environment. Hence, it is important to include environmental considerations, for example, via engaging environmental experts and sharing data. Further case studies will help to better define the roles of environment in One Health scenarios.

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Global Aerosol Classification Based on Aerosol Robotic Network (AERONET) and Satellite Observation

Remote Sensing ◽

10.3390/rs13061114 ◽

2021 ◽

Vol 13 (6) ◽

pp. 1114

Author(s):

Jianyu Lin ◽

Yu Zheng ◽

Xinyong Shen ◽

Lizhu Xing ◽

Huizheng Che

Keyword(s):

Radiative Forcing ◽

Temporal Distribution ◽

Satellite Observation ◽

Western Africa ◽

Deep Blue ◽

Sensing Technology ◽

Aerosol Classification ◽

Linear Depolarization Ratio ◽

Absorbing Aerosols ◽

Single Scatter

The particle linear depolarization ratio (PLDR) and single scatter albedo (SSA) in 1020 nm from the Aerosol Robotic Network (AERONET) level 2.0 dataset was utilized among 52 stations to identify dust and dust dominated aerosols (DD), pollution dominated mixture (PDM), strongly absorbing aerosols (SA) and weakly absorbing aerosols (WA), investigate their spatial and temporal distribution, net radiative forcing and radiative forcing efficiency in global range, and further compare with VIIRS Deep Blue Production. The conclusion about net radiative forcing suggests that the high values of radiative forcing from dust and dust dominated aerosols, pollution dominated mixture both mainly come from western Africa. Strongly absorbing aerosols in South Africa and India contribute greatly to the net radiative forcing and the regions with relative high values of weakly absorbing aerosols are mainly located at East Asia and India. Lastly, the observation of VIIRS Deep Blue satellite monthly averaged products depicts the characteristics about spatial distribution of four kinds of aerosol well, the result from ground-based observation presents great significant to validate the measurements from remote sensing technology.

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Ocean Emission Effects on Aerosol-Cloud Interactions: Insights from Two Case Studies

Advances in Meteorology ◽

10.1155/2010/301395 ◽

2010 ◽

Vol 2010 ◽

pp. 1-9 ◽

Cited By ~ 8

Author(s):

Armin Sorooshian ◽

Hanh T. Duong

Keyword(s):

Case Studies ◽

Field Experiments ◽

Remote Sensing Data ◽

Anthropogenic Pollution ◽

Cloud Microphysics ◽

Marine Aerosols ◽

Aerosol Cloud ◽

First Case ◽

Wide Range ◽

Aerosol Cloud Interactions

Two case studies are discussed that evaluate the effect of ocean emissions on aerosol-cloud interactions. A review of the first case study from the eastern Pacific Ocean shows that simultaneous aircraft and space-borne observations are valuable in detecting links between ocean biota emissions and marine aerosols, but that the effect of the former on cloud microphysics is less clear owing to interference from background anthropogenic pollution and the difficulty with field experiments in obtaining a wide range of aerosol conditions to robustly quantify ocean effects on aerosol-cloud interactions. To address these limitations, a second case was investigated using remote sensing data over the less polluted Southern Ocean region. The results indicate that cloud drop size is reduced more for a fixed increase in aerosol particles during periods of higher ocean chlorophyll A. Potential biases in the results owing to statistical issues in the data analysis are discussed.

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