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Description
Human breath is a concoction of thousands of compounds having in it a breath-print of physiological processes in the body. Though breath provides a non-invasive and easy to handle biological fluid, its analysis for clinical diagnosis is not very common. Partly the reason for this absence is unavailability of cost effective and convenient tools for such analysis. Scientific literature is full of novel sensor ideas but it is challenging to develop a working device, which are few. These challenges include trace level detection, presence of hundreds of interfering compounds, excessive humidity, different sampling regulations and personal variability. To meet these challenges as well as deliver a low cost solution, optical sensors based on specific colorimetric chemical reactions on mesoporous membranes have been developed. Sensor hardware utilizing cost effective and ubiquitously available light source (LED) and detector (webcam/photo diodes) has been developed and optimized for sensitive detection. Sample conditioning mouthpiece suitable for portable sensors is developed and integrated. The sensors are capable of communication with mobile phones realizing the idea of m-health for easy personal health monitoring in free living conditions. Nitric oxide and Acetone are chosen as analytes of interest. Nitric oxide levels in the breath correlate with lung inflammation which makes it useful for asthma management. Acetone levels increase during ketosis resulting from fat metabolism in the body. Monitoring breath acetone thus provides useful information to people with type1 diabetes, epileptic children on ketogenic diets and people following fitness plans for weight loss.
ContributorsPrabhakar, Amlendu (Author) / Tao, Nongjian (Thesis advisor) / Forzani, Erica (Committee member) / Lindsay, Stuart (Committee member) / Arizona State University (Publisher)
Created2013
Description
The world of healthcare can be seen as dynamic, often an area where technology and science meet to consummate a greater good for humanity. This relationship has been working well for the last century as evident by the average life expectancy change. For the greater of the last five decades the average life expectancy at birth increased globally by almost 20 years. In the United States specifically, life expectancy has grown from 50 years in 1900 to 78 years in 2009. That is a 76% increase in just over a century. As great as this increase sounds for humanity it means there are soon to be real issues in the healthcare world. A larger older population will need more healthcare services but have fewer young professionals to provide those services. Technology and science will need to continue to push the boundaries in order to develop and provide the solutions needed to continue providing the aging world population sufficient healthcare. One solution sure to help provide a brighter future for healthcare is mobile health (m-health). M-health can help provide a means for healthcare professionals to treat more patients with less work expenditure and do so with more personalized healthcare advice which will lead to better treatments. This paper discusses one area of m-health devices specifically; human breath analysis devices. The current laboratory methods of breath analysis and why these methods are not adequate for common healthcare practices will be discussed in more detail. Then more specifically, mobile breath analysis devices are discussed. The topic will encompass the challenges that need to be met in developing such devices, possible solutions to these challenges, two real examples of mobile breath analysis devices and finally possible future directions for m-health technologies.
ContributorsLester, Bryan (Author) / Forzani, Erica (Thesis advisor) / Xian, Xiaojun (Committee member) / Trimble, Steve (Committee member) / Arizona State University (Publisher)
Created2012
Description
Air pollution is one of the biggest challenges people face today. It is closely related to people's health condition. The agencies set up standards to regulate the air pollution. However, many of the pollutants under the regulation level may still result in adverse health effect. On the other hand, it is not clear the exact mechanism of air pollutants and its health effect. So it is difficult for the health centers to advise people how to prevent the air pollutant related diseases. It is of vital importance for both the agencies and the health centers to have a better understanding of the air pollution. Based on these needs, it is crucial to establish mobile health sensors for personal exposure assessment. Here, two sensing principles are illustrated: the tuning fork platform and the colorimetric platform. Mobile devices based on these principles have been built. The detections of ozone, NOX, carbon monoxide and formaldehyde have been shown. An integrated device of nitrogen dioxide and carbon monoxide is introduced. Fan is used for sample delivery instead pump and valves to reduce the size, cost and power consumption. Finally, the future work is discussed.
ContributorsWang, Rui (Author) / Tao, Nongjian (Thesis advisor) / Forzani, Erica (Committee member) / Zhang, Yanchao (Committee member) / Karam, Lina (Committee member) / Arizona State University (Publisher)
Created2012
Description
The aim of this study was to investigate the microstructural sensitivity of the statistical distribution and diffusion kurtosis (DKI) models of non-monoexponential signal attenuation in the brain using diffusion-weighted MRI (DWI). We first developed a simulation of 2-D water diffusion inside simulated tissue consisting of semi-permeable cells and a variable cell size. We simulated a DWI acquisition using a pulsed gradient spin echo (PGSE) pulse sequence, and fitted the models to the simulated DWI signals using b-values up to 2500 s/mm2. For comparison, we calculated the apparent diffusion coefficient (ADC) of the monoexponential model (b-value = 1000 s/mm2). In separate experiments, we varied the cell size (5-10-15 μ), cell volume fraction (0.50-0.65-0.80), and membrane permeability (0.001-0.01-0.1 mm/s) to study how the fitted parameters tracked simulated microstructural changes. The ADC was sensitive to all the simulated microstructural changes except the decrease in membrane permeability. The σstat of the statistical distribution model increased exclusively with a decrease in cell volume fraction. The Kapp of the DKI model increased exclusively with decreased cell size and decreased with increasing membrane permeability. These results suggest that the non-monoexponential models have different, specific microstructural sensitivity, and a combination of the models may give insights into the microstructural underpinning of tissue pathology. Faster PROPELLER DWI acquisitions, such as Turboprop and X-prop, remain subject to phase errors inherent to a gradient echo readout, which ultimately limits the applied turbo factor and thus scan time reductions. This study introduces a new phase correction to Turboprop, called Turboprop+. This technique employs calibration blades, which generate 2-D phase error maps and are rotated in accordance with the data blades, to correct phase errors arising from off-resonance and system imperfections. The results demonstrate that with a small increase in scan time for collecting calibration blades, Turboprop+ had a superior immunity to the off-resonance related artifacts when compared to standard Turboprop and recently proposed X-prop with the high turbo factor (turbo factor = 7). Thus, low specific absorption rate (SAR) and short scan time can be achieved in Turboprop+ using a high turbo factor, while off-resonance related artifacts are minimized.
ContributorsLee, Chu-Yu (Author) / Debbins, Josef P (Thesis advisor) / Bennett, Kevin M (Thesis advisor) / Karam, Lina (Committee member) / Pipe, James G (Committee member) / Arizona State University (Publisher)
Created2012
Description
Obesity has consistently presented a significant challenge, with excess body fat contributing to the development of numerous severe conditions such as diabetes, cardiovascular disease, cancer, and various musculoskeletal disorders. In this study, different methods are proposed to study substrate utilization (carbohydrates, proteins, and fats) in the human body and validate the biomarkers enabling to investigation of weight management and monitor metabolic health. The first technique to study was Indirect calorimetry, which assessed Resting Energy Expenditure (REE) and measured parameters like oxygen consumption (VO2) and carbon dioxide production (VCO2). A validation study was conducted to study the effectiveness of the medical device Breezing Med determining REE, VO2, and VCO2. The results were compared with correlation slopes and regression coefficients close to 1. Indirect Calorimetry can be used to determine carbohydrate and fat utilization but it requires additional correction for protein utilization. Protein utilization can be studied by analyzing urinary nitrogen. Therefore, a secondary technique was studied for identifying urea and ammonia concentration in human urine samples. Along this line two methods for detecting urea were explored, a colorimetric technique and it was validated against the Ion-Selective method. The results were then compared by correlation analysis of urine samples measured with both methods simultaneously curves. The equations for fat, carb, and protein oxidation, involving VO2, VCO2 consumption, and urinary nitrogen were implemented and validated, using the above-described methods in a human subject study with 16 subjects. The measurements included diverse diets (normal vs. high fat/protein) in normal energy balance and pre-/post interventions of exercise, fasting, and a high-fat meal. It can be concluded that the indirect calorimetry portable method in conjunction with urine urea methods are important to help the understanding of substrate utilization in human subjects, and therefore, excellent tools to contribute to the treatments and interventions of obesity and overweighted populations.
ContributorsPradhan, Ayushi (Author) / Forzani, Erica (Thesis advisor) / Lind, Mary Laura (Committee member) / Wang, Shaopeng (Committee member) / Arizona State University (Publisher)
Created2023
Description
Realtime understanding of one’s complete metabolic state is crucial to controlling weight and managing chronic illnesses, such as diabetes. This project represents the development of a novel breath acetone sensor within the Biodesign Institute’s Center for Bioelectronics and Biosensors. The purpose is to determine if a sensor can be manufactured with the capacity to measure breath acetone concentrations typical of various levels of metabolic activity. For this purpose, a solution that selectively interacts with acetone was embedded in a sensor cartridge that is permeable to volatile organic compounds. After 30 minutes of exposure to a range of acetone concentrations, a color change response was observed in the sensors. Requiring only exposure to a breath, these novel sensor configurations may offer non-trivial improvements to clinical and at-home measurement of lipid metabolic rate.
ContributorsDenham, Landon (Author) / Forzani, Erica (Thesis director) / Mora, Sabrina Jimena (Committee member) / Barrett, The Honors College (Contributor) / Chemical Engineering Program (Contributor)
Created2022-05
Description
Pervaporation is a membrane process suited to complex and highly contaminated wastewaters. Pervaporation desalination is an emerging area of study where the development of high-performance membranes is necessary to propel the field forward. This research demonstrated that sulfonated block polymer membranes (Nexar™)show excellent permeance (water passage normalized by driving force) of as much as 135.5 ± 29 kg m-2 hr-1 bar-1, with salt removal values consistently equal to or greater than 99.5%. Another challenging water management scenario is in spaceflight situations, such as on the International Space Station (ISS). Spaceflight wastewaters are highly complex, with low pH values, and high levels of contaminants. Current processes produce 70% wastewater recovery, necessitating the handling and processing of concentrated brines. Since recoveries of 85% are desired moving forward, further efforts in water recovery are desirable. An area of concern in these ISS water treatment systems is scalant deposition, especially of divalent ions such as calcium species. Zwitterions are molecules with localized positive and negative charges, but an overall neutral charge. Zwitterions have been used to modify the surface of membranes have shown to decrease fouling. Building a copolymer between zwitterions and other polymers, creates zwitterion layer on top of previously studied Nexar™ membranes. This coating demonstrates great promise to combat scaling, as it increases the hydrophilicity of the membrane surface measured via contact angle. The zwitterion membranes experienced reduced scaling, with the greatest difference being between 1617 ± 241 wt% on control membranes, to 317 ± 87 wt% on zwitterion coated membranes in the presence of CaCl2. In treating spaceflight wastewater, these zwitterion membranes are effective at retaining the acid in the feed, going from a pH value of 2 to 7 and reducing the contamination level of the feed, with a removal value of 99.3 ± 0.4%, measured through conductivity. These membranes also perform well in separation processes that do not require extreme vacuum and can be operated passively. By optimizing both membrane material properties and process conditions, achieving increased high levels of water recovery from spaceflight wastewaters is attainable.
ContributorsThomas, Elisabeth (Author) / Lind, Mary Laura (Thesis advisor) / Forzani, Erica (Committee member) / Perreault, Francois (Committee member) / Walker, W. Shane (Committee member) / Williamson, Jill P (Committee member) / Arizona State University (Publisher)
Created2023
Description
Despite the fact that machine learning supports the development of computer vision applications by shortening the development cycle, finding a general learning algorithm that solves a wide range of applications is still bounded by the ”no free lunch theorem”. The search for the right algorithm to solve a specific problem is driven by the problem itself, the data availability and many other requirements.
Automated visual inspection (AVI) systems represent a major part of these challenging computer vision applications. They are gaining growing interest in the manufacturing industry to detect defective products and keep these from reaching customers. The process of defect detection and classification in semiconductor units is challenging due to different acceptable variations that the manufacturing process introduces. Other variations are also typically introduced when using optical inspection systems due to changes in lighting conditions and misalignment of the imaged units, which makes the defect detection process more challenging.
In this thesis, a BagStack classification framework is proposed, which makes use of stacking and bagging concepts to handle both variance and bias errors. The classifier is designed to handle the data imbalance and overfitting problems by adaptively transforming the
multi-class classification problem into multiple binary classification problems, applying a bagging approach to train a set of base learners for each specific problem, adaptively specifying the number of base learners assigned to each problem, adaptively specifying the number of samples to use from each class, applying a novel data-imbalance aware cross-validation technique to generate the meta-data while taking into account the data imbalance problem at the meta-data level and, finally, using a multi-response random forest regression classifier as a meta-classifier. The BagStack classifier makes use of multiple features to solve the defect classification problem. In order to detect defects, a locally adaptive statistical background modeling is proposed. The proposed BagStack classifier outperforms state-of-the-art image classification techniques on our dataset in terms of overall classification accuracy and average per-class classification accuracy. The proposed detection method achieves high performance on the considered dataset in terms of recall and precision.
Automated visual inspection (AVI) systems represent a major part of these challenging computer vision applications. They are gaining growing interest in the manufacturing industry to detect defective products and keep these from reaching customers. The process of defect detection and classification in semiconductor units is challenging due to different acceptable variations that the manufacturing process introduces. Other variations are also typically introduced when using optical inspection systems due to changes in lighting conditions and misalignment of the imaged units, which makes the defect detection process more challenging.
In this thesis, a BagStack classification framework is proposed, which makes use of stacking and bagging concepts to handle both variance and bias errors. The classifier is designed to handle the data imbalance and overfitting problems by adaptively transforming the
multi-class classification problem into multiple binary classification problems, applying a bagging approach to train a set of base learners for each specific problem, adaptively specifying the number of base learners assigned to each problem, adaptively specifying the number of samples to use from each class, applying a novel data-imbalance aware cross-validation technique to generate the meta-data while taking into account the data imbalance problem at the meta-data level and, finally, using a multi-response random forest regression classifier as a meta-classifier. The BagStack classifier makes use of multiple features to solve the defect classification problem. In order to detect defects, a locally adaptive statistical background modeling is proposed. The proposed BagStack classifier outperforms state-of-the-art image classification techniques on our dataset in terms of overall classification accuracy and average per-class classification accuracy. The proposed detection method achieves high performance on the considered dataset in terms of recall and precision.
ContributorsHaddad, Bashar Muneer (Author) / Karam, Lina (Thesis advisor) / Li, Baoxin (Committee member) / He, Jingrui (Committee member) / Turaga, Pavan (Committee member) / Arizona State University (Publisher)
Created2019
Description
Digital imaging and image processing technologies have revolutionized the way in which
we capture, store, receive, view, utilize, and share images. In image-based applications,
through different processing stages (e.g., acquisition, compression, and transmission), images
are subjected to different types of distortions which degrade their visual quality. Image
Quality Assessment (IQA) attempts to use computational models to automatically evaluate
and estimate the image quality in accordance with subjective evaluations. Moreover, with
the fast development of computer vision techniques, it is important in practice to extract
and understand the information contained in blurred images or regions.
The work in this dissertation focuses on reduced-reference visual quality assessment of
images and textures, as well as perceptual-based spatially-varying blur detection.
A training-free low-cost Reduced-Reference IQA (RRIQA) method is proposed. The
proposed method requires a very small number of reduced-reference (RR) features. Extensive
experiments performed on different benchmark databases demonstrate that the proposed
RRIQA method, delivers highly competitive performance as compared with the
state-of-the-art RRIQA models for both natural and texture images.
In the context of texture, the effect of texture granularity on the quality of synthesized
textures is studied. Moreover, two RR objective visual quality assessment methods that
quantify the perceived quality of synthesized textures are proposed. Performance evaluations
on two synthesized texture databases demonstrate that the proposed RR metrics outperforms
full-reference (FR), no-reference (NR), and RR state-of-the-art quality metrics in
predicting the perceived visual quality of the synthesized textures.
Last but not least, an effective approach to address the spatially-varying blur detection
problem from a single image without requiring any knowledge about the blur type, level,
or camera settings is proposed. The evaluations of the proposed approach on a diverse
sets of blurry images with different blur types, levels, and content demonstrate that the
proposed algorithm performs favorably against the state-of-the-art methods qualitatively
and quantitatively.
we capture, store, receive, view, utilize, and share images. In image-based applications,
through different processing stages (e.g., acquisition, compression, and transmission), images
are subjected to different types of distortions which degrade their visual quality. Image
Quality Assessment (IQA) attempts to use computational models to automatically evaluate
and estimate the image quality in accordance with subjective evaluations. Moreover, with
the fast development of computer vision techniques, it is important in practice to extract
and understand the information contained in blurred images or regions.
The work in this dissertation focuses on reduced-reference visual quality assessment of
images and textures, as well as perceptual-based spatially-varying blur detection.
A training-free low-cost Reduced-Reference IQA (RRIQA) method is proposed. The
proposed method requires a very small number of reduced-reference (RR) features. Extensive
experiments performed on different benchmark databases demonstrate that the proposed
RRIQA method, delivers highly competitive performance as compared with the
state-of-the-art RRIQA models for both natural and texture images.
In the context of texture, the effect of texture granularity on the quality of synthesized
textures is studied. Moreover, two RR objective visual quality assessment methods that
quantify the perceived quality of synthesized textures are proposed. Performance evaluations
on two synthesized texture databases demonstrate that the proposed RR metrics outperforms
full-reference (FR), no-reference (NR), and RR state-of-the-art quality metrics in
predicting the perceived visual quality of the synthesized textures.
Last but not least, an effective approach to address the spatially-varying blur detection
problem from a single image without requiring any knowledge about the blur type, level,
or camera settings is proposed. The evaluations of the proposed approach on a diverse
sets of blurry images with different blur types, levels, and content demonstrate that the
proposed algorithm performs favorably against the state-of-the-art methods qualitatively
and quantitatively.
ContributorsGolestaneh, Seyedalireza (Author) / Karam, Lina (Thesis advisor) / Bliss, Daniel W. (Committee member) / Li, Baoxin (Committee member) / Turaga, Pavan K. (Committee member) / Arizona State University (Publisher)
Created2018
Description
This study uses Computational Fluid Dynamics (CFD) modeling to analyze the
dependence of wind power potential and turbulence intensity on aerodynamic design of a
special type of building with a nuzzle-like gap at its rooftop. Numerical simulations using
ANSYS Fluent are carried out to quantify the above-mentioned dependency due to three
major geometric parameters of the building: (i) the height of the building, (ii) the depth of
the roof-top gap, and (iii) the width of the roof-top gap. The height of the building is varied
from 8 m to 24 m. Likewise, the gap depth is varied from 3 m to 5 m and the gap width
from 2 m to 4 m. The aim of this entire research is to relate these geometric parameters of
the building to the maximum value and the spatial pattern of wind power potential across
the roof-top gap. These outcomes help guide the design of the roof-top geometry for wind
power applications and determine the ideal position for mounting a micro wind turbine.
From these outcomes, it is suggested that the wind power potential is greatly affected by
the increasing gap width or gap depth. It, however, remains insensitive to the increasing
building height, unlike turbulence intensity which increases with increasing building
height. After performing a set of simulations with varying building geometry to quantify
the wind power potential before the installation of a turbine, another set of simulations is
conducted by installing a static turbine within the roof-top gap. The results from the latter
are used to further adjust the estimate of wind power potential. Recommendations are made
for future applications based on the findings from the numerical simulations.
dependence of wind power potential and turbulence intensity on aerodynamic design of a
special type of building with a nuzzle-like gap at its rooftop. Numerical simulations using
ANSYS Fluent are carried out to quantify the above-mentioned dependency due to three
major geometric parameters of the building: (i) the height of the building, (ii) the depth of
the roof-top gap, and (iii) the width of the roof-top gap. The height of the building is varied
from 8 m to 24 m. Likewise, the gap depth is varied from 3 m to 5 m and the gap width
from 2 m to 4 m. The aim of this entire research is to relate these geometric parameters of
the building to the maximum value and the spatial pattern of wind power potential across
the roof-top gap. These outcomes help guide the design of the roof-top geometry for wind
power applications and determine the ideal position for mounting a micro wind turbine.
From these outcomes, it is suggested that the wind power potential is greatly affected by
the increasing gap width or gap depth. It, however, remains insensitive to the increasing
building height, unlike turbulence intensity which increases with increasing building
height. After performing a set of simulations with varying building geometry to quantify
the wind power potential before the installation of a turbine, another set of simulations is
conducted by installing a static turbine within the roof-top gap. The results from the latter
are used to further adjust the estimate of wind power potential. Recommendations are made
for future applications based on the findings from the numerical simulations.
ContributorsKailkhura, Gargi (Author) / Huang, Huei-Ping (Thesis advisor) / Rajagopalan, Jagannathan (Committee member) / Forzani, Erica (Committee member) / Arizona State University (Publisher)
Created2017