Matching Items (27)
Description
This thesis dissertation presents design of portable low power Electrochemical Impedance Spectroscopy (EIS) system which can be used for biomedical applications such as tear diagnosis, blood diagnosis, or any other body-fluid diagnosis. Two design methodologies are explained in this dissertation (a) a discrete component-based portable low-power EIS system and (b) an integrated CMOS-based portable low-power EIS system. Both EIS systems were tested in a laboratory environment and the characterization results are compared. The advantages and disadvantages of the integrated EIS system relative to the discrete component-based EIS system are presented including experimental data. The specifications of both EIS systems are compared with commercially available non-portable EIS workstations. These designed EIS systems are handheld and very low-cost relative to the currently available commercial EIS workstations.
ContributorsGhorband, Vishal (Author) / Blain Christen, Jennifer (Thesis advisor) / Song, Hongjiang (Committee member) / LaBelle, Jeffrey (Committee member) / Arizona State University (Publisher)
Created2016
Description
Over the past fifty years, the development of sensors for biological applications has increased dramatically. This rapid growth can be attributed in part to the reduction in feature size, which the electronics industry has pioneered over the same period. The decrease in feature size has led to the production of microscale sensors that are used for sensing applications, ranging from whole-body monitoring down to molecular sensing. Unfortunately, sensors are often developed without regard to how they will be integrated into biological systems. The complexities of integration are underappreciated. Integration involves more than simply making electrical connections. Interfacing microscale sensors with biological environments requires numerous considerations with respect to the creation of compatible packaging, the management of biological reagents, and the act of combining technologies with different dimensions and material properties. Recent advances in microfluidics, especially the proliferation of soft lithography manufacturing methods, have established the groundwork for creating systems that may solve many of the problems inherent to sensor-fluidic interaction. The adaptation of microelectronics manufacturing methods, such as Complementary Metal-Oxide-Semiconductor (CMOS) and Microelectromechanical Systems (MEMS) processes, allows the creation of a complete biological sensing system with integrated sensors and readout circuits. Combining these technologies is an obstacle to forming complete sensor systems. This dissertation presents new approaches for the design, fabrication, and integration of microscale sensors and microelectronics with microfluidics. The work addresses specific challenges, such as combining commercial manufacturing processes into biological systems and developing microscale sensors in these processes. This work is exemplified through a feedback-controlled microfluidic pH system to demonstrate the integration capabilities of microscale sensors for autonomous microenvironment control.
ContributorsWelch, David (Author) / Blain Christen, Jennifer (Thesis advisor) / Muthuswamy, Jitendran (Committee member) / Frakes, David (Committee member) / LaBelle, Jeffrey (Committee member) / Goryll, Michael (Committee member) / Arizona State University (Publisher)
Created2012
Description
In this thesis two methodologies have been proposed for evaluating the fault response of analog/RF circuits. These proposed approaches are used to evaluate the response of the faulty circuit in terms of specifications/measurements. Faulty response can be used to evaluate important test metrics like fail probability, fault coverage and yield coverage of given measurements under process variations. Once the models for faulty and fault free circuit are generated, one needs to perform Monte Carlo sampling (as opposed to Monte Carlo simulations) to compute these statistical parameters with high accuracy. The first method is based on adaptively determining the order of the model based on the error budget in terms of computing the statistical metrics and position of the threshold(s) to decide how precisely necessary models need to be extracted. In the second method, using hierarchy in process variations a hybrid of heuristics and localized linear models have been proposed. Experiments on LNA and Mixer using the adaptive model order selection procedure can reduce the number of necessary simulations by 7.54x and 7.03x respectively in the computation of fail probability for an error budget of 2%. Experiments on LNA using the hybrid approach can reduce the number of necessary simulations by 21.9x and 17x for four and six output parameters cases for improved accuracy in test statistics estimation.
ContributorsSubrahmaniyan Radhakrishnan, Gurusubrahmaniyan (Author) / Ozev, Sule (Thesis advisor) / Blain Christen, Jennifer (Committee member) / Cao, Yu (Committee member) / Arizona State University (Publisher)
Created2010
Description
Colorimetric assays are an important tool in point-of-care testing that offers several advantages to traditional testing methods such as rapid response times and inexpensive costs. A factor that currently limits the portability and accessibility of these assays are methods that can objectively determine the results of these assays. Current solutions consist of creating a test reader that standardizes the conditions the strip is under before being measured in some way. However, this increases the cost and decreases the portability of these assays. The focus of this study is to create a machine learning algorithm that can objectively determine results of colorimetric assays under varying conditions. To ensure the flexibility of a model to several types of colorimetric assays, three models were trained on the same convolutional neural network with different datasets. The images these models are trained on consist of positive and negative images of ETG, fentanyl, and HPV Antibodies test strips taken under different lighting and background conditions. A fourth model is trained on an image set composed of all three strip types. The results from these models show it is able to predict positive and negative results to a high level of accuracy.
ContributorsFisher, Rachel (Author) / Blain Christen, Jennifer (Thesis director) / Anderson, Karen (Committee member) / School of Life Sciences (Contributor) / Harrington Bioengineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
Description
For two centuries, electrical stimulation has been the conventional method for interfacing with the nervous system. As interfaces with the peripheral nervous system become more refined and higher-resolution, several challenges appear, including immune responses to invasive electrode application, large-to-small axon recruitment order, and electrode size-dependent spatial selectivity. Optogenetics offers a solution that is less invasive, more tissue-selective, and has small-to-large axon recruitment order. By adding genes to express photosensitive proteins optogenetics provides neuroscientists the ability to genetically select cell populations to stimulate with simple illumination. However, optogenetic stimulation of peripheral nerves uses diffuse light to activate the photosensitive neural cell lines. To increase the specificity of stimulus response, research was conducted to test the hypothesis that multiple, focused light emissions placed around the circumference of optogenetic mouse sciatic nerve could be driven to produce differential responses in hindlimb motor movement depending on the pattern of light presented. A Monte Carlo computer simulation was created to model the number of emitters, the light emission size, and the focal power of accompanying micro-lenses to provide targeted stimulation to select regions within the sciatic nerve. The computer simulation results were used to parameterize the design of micro-lenses. By modeling multiple focused beams, only fascicles within a nerve diameter less than 1 mm are expected to be fully accessible to focused optical stimulation; a minimum of 4 light sources is required to generate a photon intensity at a point in a nerve over the initial contact along its surface. To elicit the same effect in larger nerves, focusing lenses would require a numerical aperture > 1. Microlenses which met the simulation requirements were fabricated and deployed on a flexible nerve cuff which was used to stimulate the sciatic nerve in optogenetic mice. Motor neuron responses from this stimulation were compared to global illumination; stimulation using the optical cuff resulted in fine motor movement of the extensor muscles of the digits in the hindlimb. Increasing optical power resulted in a shift to gross motor movement of hindlimb. Finally, varying illumination intensity across the cuff showed changes in the extension of individual digits.
ContributorsFritz, Nicholas (Author) / Blain Christen, Jennifer (Thesis advisor) / Abbas, James (Committee member) / Goryll, Michael (Committee member) / Sadleir, Rosalind (Committee member) / Helms-Tillery, Stephen (Committee member) / Arizona State University (Publisher)
Created2021
Description
Neurological disorders are the leading cause of physical and cognitive declineglobally and affect nearly 15% of the current worldwide population. These disorders
include, but are not limited to, epilepsy, Alzheimer’s disease, Parkinson’s disease,
and multiple sclerosis. With the aging population, an increase in the prevalence of
neurodegenerative disorders is expected. Electrophysiological monitoring of neural
signals has been the gold standard for clinicians in diagnosing and treating neurological
disorders. However, advances in detection and stimulation techniques have paved the
way for relevant information not seen by standard procedures to be captured and
used in patient treatment. Amongst these advances have been improved analysis of
higher frequency activity and the increased concentration of alternative biomarkers,
specifically pH change, during states of increased neural activity. The design and
fabrication of devices with the ability to reliably interface with the brain on multiple
scales and modalities has been a significant challenge.
This dissertation introduces a novel, concentric, multi-scale micro-ECoG array
for neural applications specifically designed for seizure detection in epileptic patients.
This work investigates simultaneous detection and recording of adjacent neural tissue
using electrodes of different sizes during neural events. Signal fidelity from electrodes
of different sizes during in vivo experimentation are explored and analyzed to highlight
the advantages and disadvantages of using varying electrode sizes. Furthermore, the
novel multi-scale array was modified to perform multi-analyte detection experiments
of pH change and electrophysiological activity on the cortical surface during epileptic
events. This device highlights the ability to accurately monitor relevant information
from multiple electrode sizes and concurrently monitor multiple biomarkers during
clinical periods in one procedure that typically requires multiple surgeries.
ContributorsAkamine, Ian (Author) / Blain Christen, Jennifer (Thesis advisor) / Abbas, Jimmy (Committee member) / Muthuswamy, Jitendran (Committee member) / Goryll, Michael (Committee member) / Helms Tillery, Stephen (Committee member) / Arizona State University (Publisher)
Created2024
Description
Wearable technology has brought in a rapid shift in the areas of healthcare and lifestyle management. The recent development and usage of wearable devices like smart watches has created significant impact in areas like fitness management, exercise tracking, sleep quality assessment and early diagnosis of diseases like asthma, sleep apnea etc. This thesis is dedicated to the development of wearable systems and algorithms to fulfill unmet needs in the area of cardiorespiratory monitoring.
First, a pneumotach based flow sensing technique has been developed and integrated into a face mask for respiratory profile tracking. Algorithms have been developed to convert the pressure profile into respiratory flow rate profile. Gyroscope-based correction is used to remove motion artifacts that arise from daily activities. By using Principal Component Analysis, the follow-up work established a unique respiratory signature for each subject based on the flow profile and lung parameters computed using the wearable mask system.
Next, wristwatch devices to track transcutaneous gases like oxygen (TcO2) and carbon dioxide (TcCO2), and oximetry (SpO2) have been developed. Two chemical sensing approaches have been explored. In the first approach, miniaturized low-cost commercial sensors have been integrated into the wristwatch for transcutaneous gas sensing. In the second approach, CMOS camera-based colorimetric sensors are integrated into the wristwatch, where a part of camera frame is used for photoplethysmography while the remaining part tracks the optical signal from colorimetric sensors.
Finally, the wireless connectivity using Bluetooth Low Energy (BLE) in wearable systems has been explored and a data transmission protocol between wearables and host for reliable transfer has been developed. To improve the transmission reliability, the host is designed to use queue-based re-request routine to notify the wearable device of the missing packets that should be re-transmitted. This approach avoids the issue of host dependent packet losses and ensures that all the necessary information is received.
The works in this thesis have provided technical solutions to address challenges in wearable technologies, ranging from chemical sensing, flow sensing, data analysis, to wireless data transmission. These works have demonstrated transformation of traditional bench-top medical equipment into non-invasive, unobtrusive, ergonomic & stand-alone healthcare devices.
First, a pneumotach based flow sensing technique has been developed and integrated into a face mask for respiratory profile tracking. Algorithms have been developed to convert the pressure profile into respiratory flow rate profile. Gyroscope-based correction is used to remove motion artifacts that arise from daily activities. By using Principal Component Analysis, the follow-up work established a unique respiratory signature for each subject based on the flow profile and lung parameters computed using the wearable mask system.
Next, wristwatch devices to track transcutaneous gases like oxygen (TcO2) and carbon dioxide (TcCO2), and oximetry (SpO2) have been developed. Two chemical sensing approaches have been explored. In the first approach, miniaturized low-cost commercial sensors have been integrated into the wristwatch for transcutaneous gas sensing. In the second approach, CMOS camera-based colorimetric sensors are integrated into the wristwatch, where a part of camera frame is used for photoplethysmography while the remaining part tracks the optical signal from colorimetric sensors.
Finally, the wireless connectivity using Bluetooth Low Energy (BLE) in wearable systems has been explored and a data transmission protocol between wearables and host for reliable transfer has been developed. To improve the transmission reliability, the host is designed to use queue-based re-request routine to notify the wearable device of the missing packets that should be re-transmitted. This approach avoids the issue of host dependent packet losses and ensures that all the necessary information is received.
The works in this thesis have provided technical solutions to address challenges in wearable technologies, ranging from chemical sensing, flow sensing, data analysis, to wireless data transmission. These works have demonstrated transformation of traditional bench-top medical equipment into non-invasive, unobtrusive, ergonomic & stand-alone healthcare devices.
ContributorsTipparaju, Vishal Varun (Author) / Xian, Xiaojun (Thesis advisor) / Forzani, Erica (Thesis advisor) / Blain Christen, Jennifer (Committee member) / Angadi, Siddhartha (Committee member) / Arizona State University (Publisher)
Created2020
Description
Cellular assays are the backbone of biological studies - be it for tissue modeling, drug discovery, therapeutics, or diagnostics. Two-dimensional (2D) cell culture has been deployed for several decades to garner physiologically relevant information and predict data before the cost-intensive animal testing. Although 2D techniques have been valuable for cellular assays, they have a colossal limitation - they do not adequately consider the natural three-dimensional (3D) microenvironment of the cells. As a result, they sometimes provide misleading statistics. Therefore, it is important to develop a 3D model that predicts cellular behaviors and their interaction with neighboring cells and extracellular matrix (ECM) in a more realistic manner. In recent biomedical research, various platforms have been modeled to generate 3D prototypes of tissues, spheroids, in vitro that could allow the study of cellular responses resembling in vivo environments, such as matrices, scaffolds, and devices. But most of these platforms have drawbacks such as lack of spheroid size control, low yield, or high cost associated with them. On the other hand, Amikagel is a low cost, high-fidelity platform that can facilitate the convenient generation of tumor and stem cell spheroids. Furthermore, Amikabeads are aminoglycoside-derived hydrogel microbeads derived from the same monomers as Amikagel. They are a versatile platform with several chemical groups that can be exploited for encapsulating the spheroids and investigating the delivery of bioactive compounds to the cells. This thesis is focused on engineering novel 3D tumor and stem cell models generated on Amikagel and encapsulated in Amikabeads for proximal delivery of bioactive compounds and applications in regenerative medicine.
ContributorsNanda, Tanya (Author) / Rege, Kaushal (Thesis advisor) / Blain Christen, Jennifer (Committee member) / Weaver, Jessica (Committee member) / Arizona State University (Publisher)
Created2020
Description
Cameras have become commonplace with wide-ranging applications of phone photography, computer vision, and medical imaging. With a growing need to reduce size and costs while maintaining image quality, the need to look past traditional style of cameras is becoming more apparent. Several non-traditional cameras have shown to be promising options for size-constraint applications, and while they may offer several advantages, they also usually are limited by image quality degradation due to optical or a need to reconstruct a captured image. In this thesis, we take a look at three of these non-traditional cameras: a pinhole camera, a diffusion-mask lensless camera, and an under-display camera (UDC).
For each of these cases, I present a feasible image restoration pipeline to correct for their particular limitations. For the pinhole camera, I present an early pipeline to allow for practical pinhole photography by reducing noise levels caused by low-light imaging, enhancing exposure levels, and sharpening the blur caused by the pinhole. For lensless cameras, we explore a neural network architecture that performs joint image reconstruction and point spread function (PSF) estimation to robustly recover images captured with multiple PSFs from different cameras. Using adversarial learning, this approach achieves improved reconstruction results that do not require explicit knowledge of the PSF at test-time and shows an added improvement in the reconstruction model’s ability to generalize to variations in the camera’s PSF. This allows lensless cameras to be utilized in a wider range of applications that require multiple cameras without the need to explicitly train a separate model for each new camera. For UDCs, we utilize a multi-stage approach to correct for low light transmission, blur, and haze. This pipeline uses a PyNET deep neural network architecture to perform a majority of the restoration, while additionally using a traditional optimization approach which is then fused in a learned manner in the second stage to improve high-frequency features. I show results from this novel fusion approach that is on-par with the state of the art.
For each of these cases, I present a feasible image restoration pipeline to correct for their particular limitations. For the pinhole camera, I present an early pipeline to allow for practical pinhole photography by reducing noise levels caused by low-light imaging, enhancing exposure levels, and sharpening the blur caused by the pinhole. For lensless cameras, we explore a neural network architecture that performs joint image reconstruction and point spread function (PSF) estimation to robustly recover images captured with multiple PSFs from different cameras. Using adversarial learning, this approach achieves improved reconstruction results that do not require explicit knowledge of the PSF at test-time and shows an added improvement in the reconstruction model’s ability to generalize to variations in the camera’s PSF. This allows lensless cameras to be utilized in a wider range of applications that require multiple cameras without the need to explicitly train a separate model for each new camera. For UDCs, we utilize a multi-stage approach to correct for low light transmission, blur, and haze. This pipeline uses a PyNET deep neural network architecture to perform a majority of the restoration, while additionally using a traditional optimization approach which is then fused in a learned manner in the second stage to improve high-frequency features. I show results from this novel fusion approach that is on-par with the state of the art.
ContributorsRego, Joshua D (Author) / Jayasuriya, Suren (Thesis advisor) / Blain Christen, Jennifer (Thesis advisor) / Turaga, Pavan (Committee member) / Arizona State University (Publisher)
Created2020
Description
The study of soft magnetic materials has been growing in popularity in recent years. Driving this interest are new applications for traditional electrical power-management components, such as inductors and transformers, which must be scaled down to the micro and nano scale while the frequencies of operation have been scaling up to the gigahertz range and beyond. The exceptional magnetic properties of the materials make them highly effective in these small-component applications, but the ability of these materials to provide highly-effective shielding has not been so thoroughly considered. Most shielding is done with traditional metals, such as aluminum, because of the relatively low cost of the material and high workability in shaping the material to meet size and dimensional requirements.
This research project focuses on analyzing the variance in shielding effectiveness and electromagnetic field effects of a thin film of Cobalt Zirconium Tantalum Boron (CZTB) in the band of frequencies most likely to require innovative solutions to long-standing problems of noise and interference. The measurements include Near H-Field attenuation and field effects, Far Field shielding, and Backscatter. Minor variances in the thickness and layering of sputter deposition can have significant changes electromagnetic signature of devices which radiate energy through the material.
The material properties presented in this research are H-Field attenuation, H-Field Flux Orientation, Far-Field Approximation, E Field Vector Directivity, H Field Vector Directivity, and Backscatter Magnitude. The results are presented, analyzed and explained using characterization techniques. Future work includes the effect of sputter deposition orientation, application to devices, and applicability in mitigating specific noise signals beyond the 5G band.
This research project focuses on analyzing the variance in shielding effectiveness and electromagnetic field effects of a thin film of Cobalt Zirconium Tantalum Boron (CZTB) in the band of frequencies most likely to require innovative solutions to long-standing problems of noise and interference. The measurements include Near H-Field attenuation and field effects, Far Field shielding, and Backscatter. Minor variances in the thickness and layering of sputter deposition can have significant changes electromagnetic signature of devices which radiate energy through the material.
The material properties presented in this research are H-Field attenuation, H-Field Flux Orientation, Far-Field Approximation, E Field Vector Directivity, H Field Vector Directivity, and Backscatter Magnitude. The results are presented, analyzed and explained using characterization techniques. Future work includes the effect of sputter deposition orientation, application to devices, and applicability in mitigating specific noise signals beyond the 5G band.
ContributorsMiller, Phillip Carl (Author) / Yu, Hongbin (Thesis advisor) / Aberle, James T., 1961- (Committee member) / Blain Christen, Jennifer (Committee member) / Arizona State University (Publisher)
Created2019