Matching Items (33)
Description
Environmental pollution has been one of the most challenging problems in modern society and more and more health issues are now linked to environmental pollution and especially, air pollution. Certain sensitive group like patients with asthma are highly influenced by the environmental air quality and knowledge of the daily air pollution exposure is of great importance for the management and prevention of asthma attack. Hence small form factor, real time, accurate, sensitive and easy to use portable devices for environmental monitoring are of great value.
Three novel image-based methods for quantitative real time environmental monitoring were introduced and the sensing principle, sensor performances were evaluated through simulation and field tests. The first sensing principle uses surface plasmon resonance (SPR) image and home-made molecular sieve (MS) column to realize real time chemical separation and detection. SPR is sensitive and non-specific, which makes it a desirable optical method for sensitive biological and chemical sensing, the miniaturized MS column provides small area footprint and makes it possible for SPR to record images of the whole column area. The innovative and system level integration approach provide a new way for simultaneous chemical separation and detection. The second sensor uses scattered laser light, Complementary metal-oxide-semiconductor (CMOS) imager and image processing to realize real-time particulate matter (PM) sensing. Complex but low latency algorithm was developed to obtain real time information for PM including PM number, size and size distribution. The third sensor uses gradient based colorimetric sensor, absorbance light signal and image processing to realize real-time Ozone sensing and achieved high sensitivity and substantially longer lifetime compared to conventional colorimetric sensors. The platform provides potential for multi-analyte integration and large-scale consumer use as wearable device.
The three projects provide novel, state-of-the-art and sensitive solutions for environmental and personal exposure monitoring. Moreover, the sensing platforms also provide tools for clinicians and epidemiologists to conduct large scale clinical studies on the adverse health effects of pollutants on various kinds of diseases.
Three novel image-based methods for quantitative real time environmental monitoring were introduced and the sensing principle, sensor performances were evaluated through simulation and field tests. The first sensing principle uses surface plasmon resonance (SPR) image and home-made molecular sieve (MS) column to realize real time chemical separation and detection. SPR is sensitive and non-specific, which makes it a desirable optical method for sensitive biological and chemical sensing, the miniaturized MS column provides small area footprint and makes it possible for SPR to record images of the whole column area. The innovative and system level integration approach provide a new way for simultaneous chemical separation and detection. The second sensor uses scattered laser light, Complementary metal-oxide-semiconductor (CMOS) imager and image processing to realize real-time particulate matter (PM) sensing. Complex but low latency algorithm was developed to obtain real time information for PM including PM number, size and size distribution. The third sensor uses gradient based colorimetric sensor, absorbance light signal and image processing to realize real-time Ozone sensing and achieved high sensitivity and substantially longer lifetime compared to conventional colorimetric sensors. The platform provides potential for multi-analyte integration and large-scale consumer use as wearable device.
The three projects provide novel, state-of-the-art and sensitive solutions for environmental and personal exposure monitoring. Moreover, the sensing platforms also provide tools for clinicians and epidemiologists to conduct large scale clinical studies on the adverse health effects of pollutants on various kinds of diseases.
ContributorsDu, Zijian (Author) / Tao, Nongjian (Thesis advisor) / Goryll, Michael (Committee member) / Herckes, Pierre (Committee member) / Tsow, Tsing (Committee member) / Arizona State University (Publisher)
Created2019
Description
Antibiotic resistance is a very important issue that threatens mankind. As bacteria
are becoming resistant to multiple antibiotics, many common antibiotics will soon
become ineective. The ineciency of current methods for diagnostics is an important
cause of antibiotic resistance, since due to their relative slowness, treatment plans
are often based on physician's experience rather than on test results, having a high
chance of being inaccurate or not optimal. This leads to a need of faster, pointof-
care (POC) methods, which can provide results in a few hours. Motivated by
recent advances on computer vision methods, three projects have been developed
for bacteria identication and antibiotic susceptibility tests (AST), with the goal of
speeding up the diagnostics process. The rst two projects focus on obtaining features
from optical microscopy such as bacteria shape and motion patterns to distinguish
active and inactive cells. The results show their potential as novel methods for AST,
being able to obtain results within a window of 30 min to 3 hours, a much faster
time frame than the gold standard approach based on cell culture, which takes at
least half a day to be completed. The last project focus on the identication task,
combining large volume light scattering microscopy (LVM) and deep learning to
distinguish bacteria from urine particles. The developed setup is suitable for pointof-
care applications, as a large volume can be viewed at a time, avoiding the need
for cell culturing or enrichment. This is a signicant gain compared to cell culturing
methods. The accuracy performance of the deep learning system is higher than chance
and outperforms a traditional machine learning system by up to 20%.
are becoming resistant to multiple antibiotics, many common antibiotics will soon
become ineective. The ineciency of current methods for diagnostics is an important
cause of antibiotic resistance, since due to their relative slowness, treatment plans
are often based on physician's experience rather than on test results, having a high
chance of being inaccurate or not optimal. This leads to a need of faster, pointof-
care (POC) methods, which can provide results in a few hours. Motivated by
recent advances on computer vision methods, three projects have been developed
for bacteria identication and antibiotic susceptibility tests (AST), with the goal of
speeding up the diagnostics process. The rst two projects focus on obtaining features
from optical microscopy such as bacteria shape and motion patterns to distinguish
active and inactive cells. The results show their potential as novel methods for AST,
being able to obtain results within a window of 30 min to 3 hours, a much faster
time frame than the gold standard approach based on cell culture, which takes at
least half a day to be completed. The last project focus on the identication task,
combining large volume light scattering microscopy (LVM) and deep learning to
distinguish bacteria from urine particles. The developed setup is suitable for pointof-
care applications, as a large volume can be viewed at a time, avoiding the need
for cell culturing or enrichment. This is a signicant gain compared to cell culturing
methods. The accuracy performance of the deep learning system is higher than chance
and outperforms a traditional machine learning system by up to 20%.
ContributorsIriya, Rafael (Author) / Turaga, Pavan (Thesis advisor) / Wang, Shaopeng (Committee member) / Grys, Thomas (Committee member) / Zhang, Yanchao (Committee member) / Arizona State University (Publisher)
Created2020
Description
Now that home security systems are readily available at a low cost, these systems are commonly being installed to watch over homes and loved ones. These systems are fairly easy to install and can provide 4k Ultra HD resolution. The user can configure the sensitivity and areas to monitor and receive object detection notifications. Unfortunately, once the customer starts to use the system, they often find that the notifications are overwhelming and soon turn them off. After hearing the same experience from multiple friends and family I thought it would be a good topic for my thesis. I examined a top selling security system sold at a bulk retail store and have implemented improved detection techniques that advance object detection and reduce false notifications. The additional algorithms will support the processing of both near real-time streams and saved video file processing, which existing security systems do not include.
ContributorsBustillos, Adriana (Author) / Meuth, Ryan (Thesis director) / Nakamura, Mutsumi (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor)
Created2022-05