Matching Items (477)
Filtering by
- All Subjects: Biomedical Engineering
- Creators: Arizona State University
Description
Many products undergo several stages of testing ranging from tests on individual components to end-item tests. Additionally, these products may be further "tested" via customer or field use. The later failure of a delivered product may in some cases be due to circumstances that have no correlation with the product's inherent quality. However, at times, there may be cues in the upstream test data that, if detected, could serve to predict the likelihood of downstream failure or performance degradation induced by product use or environmental stresses. This study explores the use of downstream factory test data or product field reliability data to infer data mining or pattern recognition criteria onto manufacturing process or upstream test data by means of support vector machines (SVM) in order to provide reliability prediction models. In concert with a risk/benefit analysis, these models can be utilized to drive improvement of the product or, at least, via screening to improve the reliability of the product delivered to the customer. Such models can be used to aid in reliability risk assessment based on detectable correlations between the product test performance and the sources of supply, test stands, or other factors related to product manufacture. As an enhancement to the usefulness of the SVM or hyperplane classifier within this context, L-moments and the Western Electric Company (WECO) Rules are used to augment or replace the native process or test data used as inputs to the classifier. As part of this research, a generalizable binary classification methodology was developed that can be used to design and implement predictors of end-item field failure or downstream product performance based on upstream test data that may be composed of single-parameter, time-series, or multivariate real-valued data. Additionally, the methodology provides input parameter weighting factors that have proved useful in failure analysis and root cause investigations as indicators of which of several upstream product parameters have the greater influence on the downstream failure outcomes.
ContributorsMosley, James (Author) / Morrell, Darryl (Committee member) / Cochran, Douglas (Committee member) / Papandreou-Suppappola, Antonia (Committee member) / Roberts, Chell (Committee member) / Spanias, Andreas (Committee member) / Arizona State University (Publisher)
Created2011
Description
A dual-channel directional digital hearing aid (DHA) front-end using a fully differential difference amplifier (FDDA) based Microphone interface circuit (MIC) for a capacitive Micro Electro Mechanical Systems (MEMS) microphones and an adaptive-power analog font end (AFE) is presented. The Microphone interface circuit based on FDDA converts the capacitance variations into voltage signal, achieves a noise of 32 dB SPL (sound pressure level) and an SNR of 72 dB, additionally it also performs single to differential conversion allowing for fully differential analog signal chain. The analog front-end consists of 40dB VGA and a power scalable continuous time sigma delta ADC, with 68dB SNR dissipating 67u¬W from a 1.2V supply. The ADC implements a self calibrating feedback DAC, for calibrating the 2nd order non-linearity. The VGA and power scalable ADC is fabricated on 0.25 um CMOS TSMC process. The dual channels of the DHA are precisely matched and achieve about 0.5dB gain mismatch, resulting in greater than 5dB directivity index. This will enable a highly integrated and low power DHA
ContributorsNaqvi, Syed Roomi (Author) / Kiaei, Sayfe (Thesis advisor) / Bakkaloglu, Bertan (Committee member) / Chae, Junseok (Committee member) / Barnby, Hugh (Committee member) / Aberle, James T., 1961- (Committee member) / Arizona State University (Publisher)
Created2011
Description
Demand for biosensor research applications is growing steadily. According to a new report by Frost & Sullivan, the biosensor market is expected to reach $14.42 billion by 2016. Clinical diagnostic applications continue to be the largest market for biosensors, and this demand is likely to continue through 2016 and beyond. Biosensor technology for use in clinical diagnostics, however, requires translational research that moves bench science and theoretical knowledge toward marketable products. Despite the high volume of academic research to date, only a handful of biomedical devices have become viable commercial applications. Academic research must increase its focus on practical uses for biosensors. This dissertation is an example of this increased focus, and discusses work to advance microfluidic-based protein biosensor technologies for practical use in clinical diagnostics. Four areas of work are discussed: The first involved work to develop reusable/reconfigurable biosensors that are useful in applications like biochemical science and analytical chemistry that require detailed sensor calibration. This work resulted in a prototype sensor and an in-situ electrochemical surface regeneration technique that can be used to produce microfluidic-based reusable biosensors. The second area of work looked at non-specific adsorption (NSA) of biomolecules, which is a persistent challenge in conventional microfluidic biosensors. The results of this work produced design methods that reduce the NSA. The third area of work involved a novel microfluidic sensing platform that was designed to detect target biomarkers using competitive protein adsorption. This technique uses physical adsorption of proteins to a surface rather than complex and time-consuming immobilization procedures. This method enabled us to selectively detect a thyroid cancer biomarker, thyroglobulin, in a controlled-proteins cocktail and a cardiovascular biomarker, fibrinogen, in undiluted human serum. The fourth area of work involved expanding the technique to produce a unique protein identification method; Pattern-recognition. A sample mixture of proteins generates a distinctive composite pattern upon interaction with a sensing platform consisting of multiple surfaces whereby each surface consists of a distinct type of protein pre-adsorbed on the surface. The utility of the "pattern-recognition" sensing mechanism was then verified via recognition of a particular biomarker, C-reactive protein, in the cocktail sample mixture.
ContributorsChoi, Seokheun (Author) / Chae, Junseok (Thesis advisor) / Tao, Nongjian (Committee member) / Yu, Hongyu (Committee member) / Forzani, Erica (Committee member) / Arizona State University (Publisher)
Created2011
Description
Alzheimer's Disease (AD) is a debilitating neurodegenerative disease. The disease leads to dementia and loss of cognitive functions and affects about 4.5 million people in the United States. It is the 7th leading cause of death and is a huge financial burden on the healthcare industry. There are no means of diagnosing the disease before neurodegeneration is significant and sadly there is no cure that controls its progression. The protein beta-amyloid or Aâ plays an important role in the progression of the disease. It is formed from the cleavage of the Amyloid Precursor Protein by two enzymes - â and ã-secretases and is found in the plaques that are deposits found in Alzheimer brains. This work describes the generation of therapeutics based on inhibition of the cleavage by â-secretase. Using in-vitro recombinant antibody display libraries to screen for single chain variable fragment (scFv) antibodies; this work describes the isolation and characterization of scFv that target the â-secretase cleavage site on APP. This approach is especially relevant since non-specific inhibition of the enzyme may have undesirable effects since the enzyme has been shown to have other important substrates. The scFv iBSEC1 successfully recognized APP, reduced â-secretase cleavage of APP and reduced Aâ levels in a cell model of Alzheimer's Disease. This work then describes the first application of bispecific antibody therapeutics to Alzheimer's Disease. iBSEC1 scFv was combined with a proteolytic scFv that enhances the "good" pathway (á-secretase cleavage) that results in alternative cleavage of APP to generate the bispecific tandem scFv - DIA10D. DIA10D reduced APP cleavage by â-secretase and steered it towards the "good" pathway thus increasing the generation of the fragment sAPPá which is neuroprotective. Finally, treatment with iBSEC1 is evaluated for reduced oxidative stress, which is observed in cells over expressing APP when they are exposed to stress. Recombinant antibody based therapeutics like scFv have several advantages since they retain the high specificity of the antibodies but are safer since they lack the constant region and are smaller, potentially facilitating easier delivery to the brain
ContributorsBoddapati, Shanta (Author) / Sierks, Michael (Thesis advisor) / Arizona State University (Publisher)
Created2011
Description
A good production schedule in a semiconductor back-end facility is critical for the on time delivery of customer orders. Compared to the front-end process that is dominated by re-entrant product flows, the back-end process is linear and therefore more suitable for scheduling. However, the production scheduling of the back-end process is still very difficult due to the wide product mix, large number of parallel machines, product family related setups, machine-product qualification, and weekly demand consisting of thousands of lots. In this research, a novel mixed-integer-linear-programming (MILP) model is proposed for the batch production scheduling of a semiconductor back-end facility. In the MILP formulation, the manufacturing process is modeled as a flexible flow line with bottleneck stages, unrelated parallel machines, product family related sequence-independent setups, and product-machine qualification considerations. However, this MILP formulation is difficult to solve for real size problem instances. In a semiconductor back-end facility, production scheduling usually needs to be done every day while considering updated demand forecast for a medium term planning horizon. Due to the limitation on the solvable size of the MILP model, a deterministic scheduling system (DSS), consisting of an optimizer and a scheduler, is proposed to provide sub-optimal solutions in a short time for real size problem instances. The optimizer generates a tentative production plan. Then the scheduler sequences each lot on each individual machine according to the tentative production plan and scheduling rules. Customized factory rules and additional resource constraints are included in the DSS, such as preventive maintenance schedule, setup crew availability, and carrier limitations. Small problem instances are randomly generated to compare the performances of the MILP model and the deterministic scheduling system. Then experimental design is applied to understand the behavior of the DSS and identify the best configuration of the DSS under different demand scenarios. Product-machine qualification decisions have long-term and significant impact on production scheduling. A robust product-machine qualification matrix is critical for meeting demand when demand quantity or mix varies. In the second part of this research, a stochastic mixed integer programming model is proposed to balance the tradeoff between current machine qualification costs and future backorder costs with uncertain demand. The L-shaped method and acceleration techniques are proposed to solve the stochastic model. Computational results are provided to compare the performance of different solution methods.
ContributorsFu, Mengying (Author) / Askin, Ronald G. (Thesis advisor) / Zhang, Muhong (Thesis advisor) / Fowler, John W (Committee member) / Pan, Rong (Committee member) / Sen, Arunabha (Committee member) / Arizona State University (Publisher)
Created2011
Description
In this work, a novel method is developed for making nano- and micro- fibrous hydrogels capable of preventing the rejection of implanted materials. This is achieved by either (1) mimicking the native cellular environment, to exert fine control over the cellular response or (2) acting as a protective barrier, to camouflage the foreign nature of a material and evade recognition by the immune system. Comprehensive characterization and in vitro studies described here provide a foundation for developing substrates for use in clinical applications. Hydrogel dextran and poly(acrylic acid) (PAA) fibers are formed via electrospinning, in sizes ranging from nanometers to microns in diameter. While "as-electrospun" fibers are continuous in length, sonication is used to fragment fibers into short fiber "bristles" and generate nano- and micro- fibrous surface coatings over a wide range of topographies. Dex-PAA fibrous surfaces are chemically modified, and then optimized and characterized for non-fouling and ECM-mimetic properties. The non-fouling nature of fibers is verified, and cell culture studies show differential responses dependent upon chemical, topographical and mechanical properties. Dex-PAA fibers are advantageously unique in that (1) a fine degree of control is possible over three significant parameters critical for modifying cellular response: topography, chemistry and mechanical properties, over a range emulating that of native cellular environments, (2) the innate nature of the material is non-fouling, providing an inert background for adding back specific bioactive functionality, and (3) the fibers can be applied as a surface coating or comprise the scaffold itself. This is the first reported work of dex-PAA hydrogel fibers formed via electrospinning and thermal cross-linking, and unique to this method, no toxic solvents or cross-linking agents are needed to create hydrogels or for surface attachment. This is also the first reported work of using sonication to fragment electrospun hydrogel fibers, and in which surface coatings were made via simple electrostatic interaction and dehydration. These versatile features enable fibrous surface coatings to be applied to virtually any material. Results of this research broadly impact the design of biomaterials which contact cells in the body by directing the consequent cell-material interaction.
ContributorsLouie, Katherine BoYook (Author) / Massia, Stephen P (Thesis advisor) / Bennett, Kevin (Committee member) / Garcia, Antonio (Committee member) / Pauken, Christine (Committee member) / Vernon, Brent (Committee member) / Arizona State University (Publisher)
Created2011
Description
Fiber-Wireless (FiWi) network is the future network configuration that uses optical fiber as backbone transmission media and enables wireless network for the end user. Our study focuses on the Dynamic Bandwidth Allocation (DBA) algorithm for EPON upstream transmission. DBA, if designed properly, can dramatically improve the packet transmission delay and overall bandwidth utilization. With new DBA components coming out in research, a comprehensive study of DBA is conducted in this thesis, adding in Double Phase Polling coupled with novel Limited with Share credits Excess distribution method. By conducting a series simulation of DBAs using different components, we found out that grant sizing has the strongest impact on average packet delay and grant scheduling also has a significant impact on the average packet delay; grant scheduling has the strongest impact on the stability limit or maximum achievable channel utilization. Whereas the grant sizing only has a modest impact on the stability limit; the SPD grant scheduling policy in the Double Phase Polling scheduling framework coupled with Limited with Share credits Excess distribution grant sizing produced both the lowest average packet delay and the highest stability limit.
ContributorsZhao, Du (Author) / Reisslein, Martin (Thesis advisor) / McGarry, Michael (Committee member) / Fowler, John (Committee member) / Arizona State University (Publisher)
Created2011
Description
Time studies are an effective tool to analyze current production systems and propose improvements. The problem that motivated the project was that conducting time studies and observing the progression of components across the factory floor is a manual process. Four Industrial Engineering students worked with a manufacturing company to develop Computer Vision technology that would automate the data collection process for time studies. The team worked in an Agile environment to complete over 120 classification sets, create 8 strategy documents, and utilize Root Cause Analysis techniques to audit and validate the performance of the trained Computer Vision data models. In the future, there is an opportunity to continue developing this product and expand the team’s work scope to apply more engineering skills on the data collected to drive factory improvements.
ContributorsJohnson, Katelyn Rose (Co-author) / Martz, Emma (Co-author) / Chmelnik, Nathan (Co-author) / de Guzman, Lorenzo (Co-author) / Ju, Feng (Thesis director) / Courter, Brandon (Committee member) / Industrial, Systems & Operations Engineering Prgm (Contributor, Contributor) / Industrial, Systems & Operations Engineering Prgm (Contributor, Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
Description
Time studies are an effective tool to analyze current production systems and propose improvements. The problem that motivated the project was that conducting time studies and observing the progression of components across the factory floor is a manual process. Four Industrial Engineering students worked with a manufacturing company to develop Computer Vision technology that would automate the data collection process for time studies. The team worked in an Agile environment to complete over 120 classification sets, create 8 strategy documents, and utilize Root Cause Analysis techniques to audit and validate the performance of the trained Computer Vision data models. In the future, there is an opportunity to continue developing this product and expand the team’s work scope to apply more engineering skills on the data collected to drive factory improvements.
ContributorsChmelnik, Nathan (Co-author) / de Guzman, Lorenzo (Co-author) / Johnson, Katelyn (Co-author) / Martz, Emma (Co-author) / Ju, Feng (Thesis director) / Courter, Brandon (Committee member) / Industrial, Systems & Operations Engineering Prgm (Contributor, Contributor) / Industrial, Systems & Operations Engineering Prgm (Contributor, Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
Description
Biological membranes are critical to cell sustainability by selectively permeating polar molecules into the intracellular space and providing protection to the interior organelles. Biomimetic membranes (model cell membranes) are often used to fundamentally study the lipid bilayer backbone structure of the biological membrane. Lipid bilayer membranes are often supported using inorganic materials in an effort to improve membrane stability and for application to novel biosensing platforms. Published literature has shown that a variety of dense inorganic materials with various surface properties have been investigated for the study of biomimetic membranes. However, literature does not adequately address the effect of porous materials or supports with varying macroscopic geometries on lipid bilayer membrane behavior. The objective of this dissertation is to present a fundamental study on the synthesis of lipid bilayer membranes supported by novel inorganic supports in an effort to expand the number of available supports for biosensing technology. There are two fundamental areas covered including: (1) synthesis of lipid bilayer membranes on porous inorganic materials and (2) synthesis and characterization of cylindrically supported lipid bilayer membranes. The lipid bilayer membrane formation behavior on various porous supports was studied via direct mass adsorption using a quartz crystal microbalance. Experimental results demonstrate significantly different membrane formation behaviors on the porous inorganic supports. A lipid bilayer membrane structure was formed only on SiO2 based surfaces (dense SiO2 and silicalite, basic conditions) and gamma-alumina (acidic conditions). Vesicle monolayer adsorption was observed on gamma-alumina (basic conditions), and yttria stabilized zirconia (YSZ) of varying roughness. Parameters such as buffer pH, surface chemistry and surface roughness were found to have a significant impact on the vesicle adsorption kinetics. Experimental and modeling work was conducted to study formation and characterization of cylindrically supported lipid bilayer membranes. A novel sensing technique (long-period fiber grating refractometry) was utilized to measure the formation mechanism of lipid bilayer membranes on an optical fiber. It was found that the membrane formation kinetics on the fiber was similar to its planar SiO2 counterpart. Fluorescence measurements verified membrane transport behavior and found that characterization artifacts affected the measured transport behavior.
ContributorsEggen, Carrie (Author) / Lin, Jerry Y.S. (Thesis advisor) / Dai, Lenore (Committee member) / Rege, Kaushal (Committee member) / Thornton, Trevor (Committee member) / Vogt, Bryan (Committee member) / Arizona State University (Publisher)
Created2011