Electrical & Computer Engineering
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Item INGESTIBLE BIOIMPEDANCE SENSING DEVICE FOR GASTROINTESTINAL TRACT MONITORING(2024) Holt, Brian Michael; Ghodssi, Reza; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Gastrointestinal (GI) diseases, such as inflammatory bowel disease (IBD), result in dilated adherens and tight junctions, altering mucosal tissue permeability. Few monitoring techniques have been developed for in situ monitoring of local mucosal barrier integrity, and none are capable of non-invasive measurement beyond the esophagus. In this work, this technology gap is addressed through the development of a noise-resilient, flexible bioimpedance sensor integrated ingestible device containing electronics for low-power, four-wire impedance measurement and Bluetooth-enabled wireless communication. Through electrochemical deposition of a conductive polymeric film, the sensor charge transfer capacity is increased 51.4-fold, enabling low-noise characterization of excised intestinal tissues with integrated potentiostat circuitry for the first time. A rodent animal trial is performed, demonstrating successful differentiation of healthy and permeable mice colonic tissues using the developed device. In accordance with established mucosal barrier evaluation methodologies, mucosal impedance was reduced between 20.3 ± 9.0% and 53.6 ± 10.7% of its baseline value in response to incrementally induced tight junction dilation. Ultimately, this work addresses the fundamental challenges of electrical resistance techniques hindering localized, non-invasive IBD diagnostics. Through the development of a simple and reliable bioimpedance sensing module, the device marks significant progress towards explicit quantification of “leaky gut” patterns in the GI tract.Item Methods and Tools for Real-Time Neural Image Processing(2023) Xie, Jing; Bhattacharyya, Shuvra; Chen, Rong; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)As a rapidly developing form of bioengineering technology, neuromodulationsystems involve extracting information from signals that are acquired from the brain and utilizing the information to stimulate brain activity. Neuromodulation has the potential to treat a wide range of neurological diseases and psychiatric conditions, as well as the potential to improve cognitive function. Neuromodulation integrates neural decoding and stimulation. As one of the twocore parts of neuromodulation systems, neural decoding subsystems interpret signals acquired through neuroimaging devices. Neuroimaging is a field of neuroscience that uses imaging techniques to study the structure and function of the brain and other central nervous system functions. Extracting information from neuroimaging signals, as is required in neural decoding, involves key challenges due to requirements of real-time, energy-efficient, and accurate processing and for large-scale, high resolution image data that are characteristic of neuromodulation systems. To address these challenges, we develop new methods and tools for design andimplementation of efficient neural image processing systems. Our contributions are organized along three complementary directions. First, we develop a prototype system for real-time neuron detection and activity extraction called the Neuron Detection and Signal Extraction Platform (NDSEP). This highly configurable system processes neural images from video streams in real-time or off-line, and applies techniques of dataflow modeling to enable extensibility and experimentation with a wide variety of image processing algorithms. Second,we develop a parameter optimization framework to tune the performance of neural image processing systems. This framework, referred to as the NEural DEcoding COnfiguration (NEDECO) package, automatically optimizes arbitrary collections of parameters in neural image processing systems under customizable constraints. The framework allows system designers to explore alternative neural image processing trade-offs involving execution time and accuracy. NEDECO is also optimized for efficient operation on multicore platforms, which allows for faster execution of the parameter optimization process. Third, we develop a neural network inference engine targeted to mobile devices.The framework can be applied to neural network implementation in many application areas, including neural image processing. The inference engine, called ShaderNN, is the first neural network inference engine that exploits both graphics-centric abstractions (fragment shaders) and compute-centric abstractions (compute shaders). The integration of fragment shaders and compute shaders makes improved use of the parallel computing advantages of GPUs on mobile devices. ShaderNN has favorable performance especially in parametrically small models.Item Lab-on-CMOS Sensors and Real-time Imaging for Biological Cell Monitoring(2019) Senevirathna, Bathiya; Abshire, Pamela; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Monitoring biological cell growth and viability is essential for in vivo biomedical diagnosis and therapy, and in vitro studies of pharmaceutical efficacy and material toxicity. Conventional monitoring techniques involve the use of dyes and markers that can potentially introduce side effects into the cell culture and often function as end-point assays. This eliminates the opportunity to track fast changes and to determine temporal correlation between measurements. Particularly in drug screening applications, high-temporal resolution cell viability data could inform decisions on drug application protocols that could lead to better treatment outcomes. This work presents development of a lab-on-chip (LoC) sensor for real-time monitoring of biological cell viability and proliferation, to provide a comprehensive picture of the changes cells undergo during their lifecycle. The LoC sensor consists of a complementary metal-oxide-semiconductor (CMOS) chip that measures the cell-to-substrate coupling of adherent cells that are cultured directly on top. This technique is non-invasive, does not require biochemical labeling, and allows for automated and unsupervised cell monitoring. The CMOS capacitance sensor was designed to addresses the ubiquitous challenges of sensitivity, noise coupling, and dynamic range that affect existing sensors. The design includes on-chip digitization, serial data output, and programmable control logic in order to facilitate packaging requirements for biological experiments. Only a microcontroller is required for readout, making it suitable for applications outside the traditional laboratory setting. An imaging platform was developed to provide time-lapse images of the sensor surface, which allowed for concurrent visual and capacitance observation of the cells. Results showed the ability of the LoC sensor to detect single cell binding events and changes in cell morphology. The sensor was used in in vitro experiments to monitor chemotherapeutic agent potency on drug-resistant and drug-sensitive cancer cell lines. Concentrations higher than 5 μM elicited cytotoxic effects on both cell lines, while a dose of 1 μM allowed discrimination of the two cell types. The system demonstrates the use of real-time capacitance measurements as a proof-of-concept tool that has potential to hasten the drug development process.Item INTEGRATED THRESHOLD-ACTIVATED FEEDBACK MICROSYSTEM FOR REAL-TIME CHARACTERIZATION, SENSING AND TREATMENT OF BACTERIAL BIOFILMS(2016) Subramanian, Sowmya; Ghodssi, Reza; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Biofilms are the primary cause of clinical bacterial infections and are impervious to typical amounts of antibiotics, necessitating very high doses for treatment. Therefore, it is highly desirable to develop new alternate methods of treatment that can complement or replace existing approaches using significantly lower doses of antibiotics. Current standards for studying biofilms are based on end-point studies that are invasive and destroy the biofilm during characterization. This dissertation presents the development of a novel real-time sensing and treatment technology to aid in the non-invasive characterization, monitoring and treatment of bacterial biofilms. The technology is demonstrated through the use of a high-throughput bifurcation based microfluidic reactor that enables simulation of flow conditions similar to indwelling medical devices. The integrated microsystem developed in this work incorporates the advantages of previous in vitro platforms while attempting to overcome some of their limitations. Biofilm formation is extremely sensitive to various growth parameters that cause large variability in biofilms between repeated experiments. In this work we investigate the use of microfluidic bifurcations for the reduction in biofilm growth variance. The microfluidic flow cell designed here spatially sections a single biofilm into multiple channels using microfluidic flow bifurcation. Biofilms grown in the bifurcated device were evaluated and verified for reduced biofilm growth variance using standard techniques like confocal microscopy. This uniformity in biofilm growth allows for reliable comparison and evaluation of new treatments with integrated controls on a single device. Biofilm partitioning was demonstrated using the bifurcation device by exposing three of the four channels to various treatments. We studied a novel bacterial biofilm treatment independent of traditional antibiotics using only small molecule inhibitors of bacterial quorum sensing (analogs) in combination with low electric fields. Studies using the bifurcation-based microfluidic flow cell integrated with real-time transduction methods and macro-scale end-point testing of the combination treatment showed a significant decrease in biomass compared to the untreated controls and well-known treatments such as antibiotics. To understand the possible mechanism of action of electric field-based treatments, fundamental treatment efficacy studies focusing on the effect of the energy of the applied electrical signal were performed. It was shown that the total energy and not the type of the applied electrical signal affects the effectiveness of the treatment. The linear dependence of the treatment efficacy on the applied electrical energy was also demonstrated. The integrated bifurcation-based microfluidic platform is the first microsystem that enables biofilm growth with reduced variance, as well as continuous real-time threshold-activated feedback monitoring and treatment using low electric fields. The sensors detect biofilm growth by monitoring the change in impedance across the interdigitated electrodes. Using the measured impedance change and user inputs provided through a convenient and simple graphical interface, a custom-built MATLAB control module intelligently switches the system into and out of treatment mode. Using this self-governing microsystem, in situ biofilm treatment based on the principles of the bioelectric effect was demonstrated by exposing two of the channels of the integrated bifurcation device to low doses of antibiotics.Item Integration of virus-like particle macromolecular bioreceptors in electrochemical biosensors(2016) Zang, Faheng; Ghodssi, Reza; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Rapid, sensitive and selective detection of chemical hazards and biological pathogens has shown growing importance in the fields of homeland security, public safety and personal health. In the past two decades, efforts have been focusing on performing point-of-care chemical and biological detections using miniaturized biosensors. These sensors convert target molecule binding events into measurable electrical signals for quantifying target molecule concentration. However, the low receptor density and the use of complex surface chemistry in receptors immobilization on transducers are common bottlenecks in the current biosensor development, adding to the cost, complexity and time. This dissertation presents the development of selective macromolecular Tobacco mosaic virus-like particle (TMV VLP) biosensing receptor, and the microsystem integration of VLPs in microfabricated electrochemical biosensors for rapid and performance-enhanced chemical and biological sensing. Two constructs of VLPs carrying different receptor peptides targeting at 2,4,6-trinitrotoluene (TNT) explosive or anti-FLAG antibody are successfully bioengineered. The VLP-based TNT electrochemical sensor utilizes unique diffusion modulation method enabled by biological binding between target TNT and receptor VLP. The method avoids the influence from any interfering species and environmental background signals, making it extremely suitable for directly quantifying the TNT level in a sample. It is also a rapid method that does not need any sensor surface functionalization process. For antibody sensing, the VLPs carrying both antibody binding peptides and cysteine residues are assembled onto the gold electrodes of an impedance microsensor. With two-phase immunoassays, the VLP-based impedance sensor is able to quantify antibody concentrations down to 9.1 ng/mL. A capillary microfluidics and impedance sensor integrated microsystem is developed to further accelerate the process of VLP assembly on sensors and improve the sensitivity. Open channel capillary micropumps and stop-valves facilitate localized and evaporation-assisted VLP assembly on sensor electrodes within 6 minutes. The VLP-functionalized impedance sensor is capable of label-free sensing of antibodies with the detection limit of 8.8 ng/mL within 5 minutes after sensor functionalization, demonstrating great potential of VLP-based sensors for rapid and on-demand chemical and biological sensing.Item INTEGRATION OF CMOS TECHNOLOGY INTO LAB-ON-CHIP SYSTEMS APPLIED TO THE DEVELOPMENT OF A BIOELECTRONIC NOSE(2015) Datta-Chaudhuri, Timir Baran; Abshire, Pamela A; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)This work addresses the development of a lab-on-a-chip (LOC) system for olfactory sensing. The method of sensing employed is cell-based, utilizing living cells to sense stimuli that are otherwise not easily sensed using conventional transduction techniques. Cells have evolved over millions of years to be exquisitely sensitive to their environment, with certain types of cells producing electrical signals in response to stimuli. The core device that is introduced here is comprised of living olfactory sensory neurons (OSNs) on top of a complementary metal-oxide-semiconductor (CMOS) integrated circuit (IC). This hybrid bioelectronic approach to sensing leverages the sensitivity of OSNs with the electronic signal processing capability of modern ICs. Intimately combining electronics with biology presents a number of unique challenges to integration that arise from the disparate requirements of the two separate domains. Fundamentally the obstacles arise from the facts that electronic devices are designed to work in dry environments while biology requires not only a wet environment, but also one that is precisely controlled and non-toxic. Design and modeling of such heterogeneously integrated systems is complicated by the lack of tools that can address the multiple domains and techniques required for integration, namely IC design, fluidics, packaging, and microfabrication, and cell culture. There also arises the issue of how to handle the vast amount of data that can be generated by such systems, and specifically how to efficiently identify signals of interest and communicate them off-chip. The primary contributions of this work are the development of a new packaging scheme for integration of CMOS ICs into fluidic LOC systems, a methodology for cross-coupled multi-domain iterative modeling of heterogeneously integrated systems, demonstration of a proof-of-concept bioelectronic olfactory sensor, and a novel event-based technique to minimize the bandwidth required to communicate the information contained in bio-potential signals produced by dense arrays of electrically active cells.Item Characterization and Application of Angled Fluorescence Laminar Optical Tomography(2013) Chen, Chaowei; Chen, Yu; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Angled fluorescence laminar optical tomography (aFLOT) is a modified fluorescence tomographic imaging technique that targets the mesoscopic scale (millimeter penetration with resolution in the tens of microns). Traditional FLOT uses multiple detectors to measure a range of scattered fluorescence signals to perform 3D reconstructions. This technology however inherently assumes the sample to be scattering. To extend the capability of FLOT to cover the low scattering regime, the oblique illumination and detection was introduced. The angular degree of freedom for the illumination and detection was theoretically and experimentally investigated. It was concluded that aFLOT enhanced resolution 2.5 times and depth selectivity compared to traditional FLOT, and that it enabled the stacking representation, a process that skips the computationally-intensive reconstruction usually needed to render the tomogram. Because stacking is enabled, the necessity of a reconstruction process is retrospectively discussed. aFLOT systems were constructed and applied in tissue engineering. Phantoms and engineered tissue models were successfully imaged. The aFLOT was shown to perform non-invasive in situ imaging in biologically relevant samples with 1mm penetration and 9-400 micron resolution, depending on the scattering of samples. aFLOT illustrates its potential for studying cell-cell or cell-material interactions.Item Decoding of walking kinematics from non-invasively acquired electroencephalographic signals in stroke patients(2012) Nathan, Kevin; Contreras-Vidal, Jose L; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Our group has recently shown the feasibility of decoding kinematics of controlled walking from the lower frequency range of electroencephalographic (EEG) signals during a precision walking task. Here, we turn our attention to stroke survivors who have had lesions resulting in hemiparetic gait. We recorded the EEG of stroke recovery patients during a precision treadmill walking task while tracking bilaterally the kinematics of the hips, knees, and ankles. In offline analyses, we applied a Wiener Filter and two unscented Kalman filters of 1st and 10th orders to predict estimates of the kinematic parameters from scalp EEG. Decoding accuracies from four patients who have had cortical and subcortical strokes were comparable with previous studies in healthy subjects. With improved decoding of EEG signals from damaged brains, we hope we can soon correlate activity to more intentional and normal-form walking that can guide users of a powered lower-body prosthetic or exoskeleton.Item Diffusion Kurtosis Magnetic Resonance Imaging and Its Application to Traumatic Brain Injury(2011) Zhuo, Jiachen; Simon, Jonathan Z; Gullapalli, Rao P; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Diffusion tensor imaging (DTI) is a popular magnetic resonance imaging technique that provides in vivo information about tissue microstructure, based on the local water diffusion environment. DTI models the diffusion displacement of water molecules in tissue as a Gaussian distribution. In this dissertation, to mimic the complex nature of water diffusion in brain tissues, a diffusion kurtosis model is used, to incorporate important non-Gaussian diffusion properties. This diffusion kurtosis imaging (DKI) is applied in an experimental traumatic brain injury in a rat model, to study whether it provides more information on microstructural changes than standard DTI. Our results indicate changes in ordinary DTI parameters, in various brain regions following injury, normalize to the baseline by the sub-acute stage. However, DKI parameters continue to show abnormalities at this sub-acute stage, as confirmed by immunohistochemical examination. Specifically, increased mean kurtosis (MK) was found to associate with increased reactive astrogliosis, a hallmark for inflammation, even in regions far removed from the injury foci. Findings suggest that monitoring changes in MK enhances the investigation of molecular and morphological changes in vivo. Extending DKI to clinical usage, however, poses several challenges: (a) long image acquisition time (~20 min) due to the augmented measurements required to fit the more complex model, (b) slow image reconstruction (~90 min) due to required nonlinear fitting and, (c) errors associated with fitting the inherently low signal-to-noise ratio (SNR) images from higher diffusion weighting. The second portion of this dissertation is devoted to developing imaging schemes and image reconstruction methods that facilitate clinical DKI applications. A fast and efficient DKI reconstruction method is developed with a reconstruction time of 2-3 seconds, with improved accuracy and reduced variability in DKI estimation over conventional methods. Further analysis of diffusion weighted imaging schemes and their affect on DKI estimation leads to the identification of two clinically practical optimal imaging schemes (needing 7-10 min) that perform comparably to traditional schemes. The effect of SNR and reconstruction methods on DKI estimation is also studied, to provide a foundation for interpreting DKI results and optimizing DKI protocols.Item Laser Beam Propagation through Scattering Medium for Sub-surface Laser Dosimetry(2011) Naphas, Renee Danielle; Chen, Yu; Ilev, Ilko K; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Optical therapeutic (OT) devices, which range in applications from laser tissue ablation and surgery to photodynamic (PDT) and low-level laser therapies (LLLT), are assessed for safety and efficacy on the basis of Maximum Permissible Exposure (MPE), which measures radiation dose in J/cm2, delivered to the target area as well as surrounding tissues. We present the characterization of an imaging system for, and method of, determining the maximum dose of devices capable of delivering peak energy level to sub-surface tissue layers. This method utilizes a fiber optic based imaging system designed to allow for comparability across laser parameters, tissue sample type and layer thickness.