Thesis M.S. Michigan State University. Electrical Engineering 2012.

This thesis describes the design, implementation and testing of self-powered, large dynamic-range, micro-data-logger that can be used for sensing, computing and non-volatile data storage of mechanical-strain statistics. At the core of the proposed design is a linear floating-gate injector that can achieve more than 13 bits of precision in sensing, signal integration and non-volatile storage. The injectors are self-powered by the piezoelectric transducers that convert mechanical energy from strain-variations into electrical energy. The first fundamental contribution of this thesis is a novel differential injector topology that is used to measure static-strain by integrating the difference between the L1 measure of the piezoelectric signal generated during the positive and negative strain-cycles. The second fundamental contribution of this thesis is a novel compressive self-powering technique that overcomes the input threshold effect of most self-powered sensors. By using a non-linear impedance circuit at the output of the piezoelectric transducer and by using programmable level-crossing circuit and offset cancellation circuits, the thesis demonstrates an extended self-powering range greater than 40dB. A system-on-chip solution has been designed that integrates the linear floating-gate injectors with high-voltage charge-pumps, digital calibration and digital programming circuits. Extensive experiments with the system-on-chip prototypes fabricated in a 0.5μm standard CMOS process and piezoelectric material (PZT) have been performed using a bench-top mechanical test setup. An automated programming and calibration of the sensor has been developed comprising of an FPGA and MATLAB interface and the results have been calibrated against standard strain-gauge measurements. This evaluation and test platform is useful for long-term, automated reliability testing of the self-powered piezo-floating-gate sensors as demonstrated in the results presented in this work.

Description based on online resource; title from PDF t.p. (ProQuest, viewed Feb. 28, 2013)

Thesis Ph. D. Michigan State University. Electrical Engineering 2012.

Functional brain networks underlying cognitive control processes have been of central interest in neuroscience. A great deal of empirical and theoretical work now suggests that frontal networks in particular the medial prefrontal cortex (mPFC) and lateral prefrontal cortex (lPFC) are involved in cognitive control. The most common way to study functional brain networks has been through measures of connectivity such as coherence, synchrony and mutual information. However, it has been noted that functional connectivity measures are limited to quantifying pairwise relationships between brain regions and do not describe the overall organization of the brain network. Recently, researchers have adapted tools from graph theory to address this issue. Graph theory can model a network by a set of vertices and edges upon which complex network analysis may be applied. With respect to the functional brain network, the vertices represent the individual neural assemblies and the edges are weighted by their pair-wise phase synchrony. Most graph theoretic measures, however, are limited to sparsely connected unweighted graphs. Therefore, some of the existing graph measures cannot be directly applied to the fully connected weighted graphs.In this thesis, existing graph measures and graph theoretic approaches are modified specifically for the analysis of the functional brain network. First, new weighted clustering coefficient and path length measures are introduced for quantifying the local weighted `small-world' index of the brain. These measures are based on modeling the edge weights as probabilities which represent the reliability of information flowing across these edges. These measures differ from conventional measures by considering all possible connections with varying strengths of connectivity and do notrequire arbitrary thresholding of the weighted connectivity matrix, i.e. they can be applied directly to a fully connected weighted graph. Next, concepts from signal processing are adapted to graphs to identify central vertices and anomalies within a network. These measures include new graph energy and entropy measures for graphs. The proposed graph energy measure outperforms existing definitions of graph energy for local anomaly detection because it is computed from the most relevant spectral content extracted from the graph's Laplacian matrix. A new definition of entropy rate based on modeling the adjacency matrix of a graph as a Markov process is introduced to quantify the local complexity of a weighted graph. Finally, we introduce a hierarchical consensus clustering algorithm that uses the well-known Fiedler vector to reveal a hierarchical structure of the brain network across various modular resolutions.The proposed methods are applied to error-related negativity (ERN) data, a response-locked negative deflection of the brain event-related potential observed following errors in performance tasks. Previous research shows that the primary neural generator…

Thesis Ph. D. Michigan State University. Electrical Engineering - Doctor of Philosophy 2014.

Blind Source Separation (BSS) is a vital unsupervised stochastic area that seeks to estimate the underlying source signals from their mixtures with minimal assumptions about the source signals and/or the mixing environment. BSS has been an active area of research and in recent years has been applied to numerous domains including biomedical engineering, image processing, wireless communications, speech enhancement, remote sensing, etc. Most recently, Independent Component Analysis (ICA) has become a vital analytical approach in BSS. In spite of active research in BSS, however, many foundational issues still remain in regards to convergence speed, performance quality and robustness in realistic or adverse environments. Furthermore, some of the developed BSS methods are computationally expensive, sensitive to additive and background noise, and not suitable for a real4time or real world implementation. In this thesis, we first formulate new effective ICA4based measures and their corresponding robust adaptive algorithms for the BSS in dynamic "convolutive mixture" environments. We demonstrate their superior performance to present competing algorithms. Then we tailor their application within wireless (CDMA) communication systems and Acoustic Separation Systems. We finally explore a system realization of one of the developed algorithms among ASIC or FPGA platforms in terms of real time speed, effectiveness, cost, and economics of scale. Firstly, we propose a new class of divergence measures for Independent Component Analysis (ICA) for estimating sources from mixtures. The Convex Cauchy4Schwarz Divergence (CCS4DIV) is formed by integrating convex functions into the Cauchy4Schwarz inequality. The new measure is symmetric and convex with respect to the joint probability, where the degree of convexity can be tuned by a (convexity) parameter. A non4parametric (ICA) algorithm generated from the proposed divergence is developed exploiting convexity parameters and employing the Parzen window4based distribution estimates. The new contrast function results in effective parametric and non4parametric ICA4based computational algorithms. Moreover, two pairwise iterative schemes are proposed to tackle the high dimensionality of sources. Secondly, a new blind detection algorithm, based on fourth order cumulant matrices, is presented and applied to the multi4user symbol estimation problem in Direct Sequence Code Division Multiple Access (DS4CDMA) systems. In addition, we propose three new blind receiver schemes, which are based on the state space structures. These so4called blind state4space receivers (BSSR) do not require knowledge of the propagation parameters or spreading code sequences of the users but relies on the statistical independence assumption among the source signals. Lastly, system realization of one of the developed algorithms has been explored among ASIC or FPGA platforms in terms of cost, effectiveness, and economics of scale.…

Thesis Ph. D. Michigan State University. Electrical Engineering 2015.

This thesis examines the design noise robust information retrieval techniques basedon kernel methods. Algorithms are presented for two biosensing applications: (1)High throughput protein arrays and (2) Non-invasive respiratory signal estimation.Our primary objective in protein array design is to maximize the throughput byenabling detection of an extremely large number of protein targets while using aminimal number of receptor spots. This is accomplished by viewing the proteinarray as a communication channel and evaluating its information transmission capacity as a function of its receptor probes. In this framework, the channel capacitycan be used as a tool to optimize probe design; the optimal probes being the onesthat maximize capacity. The information capacity is first evaluated for a small scaleprotein array, with only a few protein targets. We believe this is the first effort toevaluate the capacity of a protein array channel. For this purpose models of theproteomic channel's noise characteristics and receptor non-idealities, based on experimental prototypes, are constructed. Kernel methods are employed to extend thecapacity evaluation to larger sized protein arrays that can potentially have thousandsof distinct protein targets. A specially designed kernel which we call the ProteomicKernel is also proposed. This kernel incorporates knowledge about the biophysicsof target and receptor interactions into the cost function employed for evaluation of channel capacity.For respiratory estimation this thesis investigates estimation of breathing-rateand lung-volume using multiple non-invasive sensors under motion artifact and highnoise conditions. A spirometer signal is used as the gold standard for evaluation oferrors. A novel algorithm called the segregated envelope and carrier (SEC) estimation is proposed. This algorithm approximates the spirometer signal by an amplitudemodulated signal and segregates the estimation of the frequency and amplitude in-formation. Results demonstrate that this approach enables effective estimation ofboth breathing rate and lung volume. An adaptive algorithm based on a combination of Gini kernel machines and wavelet filltering is also proposed. This algorithm is titledthe wavelet-adaptive Gini (or WAGini) algorithm, it employs a novel wavelet trans-form based feature extraction frontend to classify the subject's underlying respiratorystate. This information is then employed to select the parameters of the adaptive kernel machine based on the subject's respiratory state. Results demonstrate significantimprovement in breathing rate estimation when compared to traditional respiratoryestimation techniques.

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Thesis Ph. D. Michigan State University, Electrical Engineering 2012.

Achieving high energy-efficiency is a key requirement for many emerging smart sensors and portable computing systems. While digital signal processing (DSP) has been the de-facto technique for implementing ultra-low power systems, analog signal processing (ASP) provides an attractive and alternate approach that can not only achieve high energy efficiency but also high computational density. Conventional ASP techniques are based on a top-down design approach, where proven mathematical principles and related algorithms are mapped and emulated using computational primitives inherent in the device physics. An example being the translinear principle, which is the state-of-the-art ASP technique, that uses the exponential current-to-voltage characteristics for designing ultra-low-power analog processors. However, elegant formulations could result from a bottom-up approach where device and bias independent computational primitives (e.g. current and charge conservation principles) are used for designing "approximate" analog signal processors. The hypothesis of this proposal is that many signal processing algorithms exhibit an inherent calibration ability due to which their performance remains unaffected by the use of "approximate" analog computing techniques. In this research, we investigate the theory, synthesis and implementation of high performance analog processors using a novel piecewise-linear (PWL) approximation algorithm called margin propagation (MP). MP principle utilizes only basic conservation laws of physical quantities (current, charge, mass, energy) for computing and therefore is scalable across devices (silicon, MEMS, microfluidics). However, there are additional advantages of MP-based processors when implemented using CMOS current-mode circuits, which includes: 1) the operation of the MP processor requires only addition, subtraction and threshold operations and hence is independent of transistor biasing (weak, moderate and strong inversion) and robust to variations in environmental conditions (e.g. temperature); and 2) improved dynamic range and faster convergence as compared to the translinear implementations. We verify our hypothesis using two analog signal processing applications: (a) design of high-performance analog low-density parity check (LDPC) decoders for applications in sensor networks; and (b) design of ultra-low-power analog support vector machines (SVM) for smart sensors. Our results demonstrate that an algorithmic framework for designing margin propagation (MP) based LDPC decoders can be used to trade-off its BER performance with its energy efficiency, making the design attractive for applications with adaptive energy-BER constraints. We have verified this trade-off using an analog current-mode implementation of an MP-based (32,8) LDPC decoder. Measured results from prototypes fabricated in a 0.5 μm CMOS process show that the BER performance of the MP-based decoder outperforms a benchmark state-of-the-art min-sum…

Thesis Ph. D. Michigan State University. Electrical Engineering 2011.

Wireless sensor systems have been widely used for both industrial and civil applications. With the development of circuit design and fabrication technique, sensor nodes now can be implemented with small scale at low cost, which is promising for ubiquitous sensing. However, with more functions integrated, the conflict between power consumption and expected lifetime became critical. Sensor nodes powered with batteries are generally compromised by extra physical size and periodic battery replacement. Therefore, energy harvesting techniques are intensively involved in sensor design where environmental signal acts as auxiliary energy source.A typical energy harvesting sensor consists of four parts: energy harvester, energy storage, power management and sensor subsystem. Energy harvester scavenges power from environmental signal which is then transferred into energy storage. Since the output power is usually not in appropriate form, power management is used to provide a usable supply voltage/current for sensor subsystem. The limitation of energy harvesting sensor is determined by the power consumption of sensor subsystem, the efficiency of energy conversion and the available energy level from environment.In this dissertation, a novel solution referred as "self-powered sensor" is proposed to extend the limitation of energy harvesting sensor. The proposed sensor can directly harvest energy from input signal being sensed. Therefore the usage of energy storage and power management is eliminated, which achieves higher energy efficiency.To demonstrate proposed solution, the system and circuit design of a self-powered sensor are presented for long-term ambient vibration monitoring. Constrained by its application, the sensor can only scavenge energy from input strain signal itself, in which scenario all existing energy harvesting techniques fail. The greatest design challenge is to achieve both ultra-low power computation and non-volatile storage. In this dissertation, a novel technique based on floating-gate transistor is presented. By exploiting controllable hot electron injection procedure, specific computation can be performed according to the characteristic of input signal. In addition, floating-gates can also retain computation results with no power consumption.For autonomous sensing, a hybrid energy harvesting topology is proposed on system level. The sensor is designed with two different operation modes. In self-powered sensing mode, it can perform continuous monitoring, computation and data storage which is powered by input strain signal. In data interrogating mode, additional functions such as data sampling and wireless communication can be enabled once a certain reading device is provided.The dissertation is organized as follows. In chapter 1, the history of wireless sensor system is reviewed. The motivation of self-powered sensor and the contributions of this dissertation are presented. Existing energy harvesting techniques are evaluated in…

Thesis Ph. D. Michigan State University. Electrical Engineering 2012.

One of several emerging areas where micro-scale integration promises significant breakthroughs is in the field of acoustic sensing. However, separation, localization, and recognition of acoustic sources using micro-scale microphone arrays poses a significant challenge due to fundamental limitations imposed by the physics of sound propagation. The smaller the distance between the recording elements, the more difficult it is to measure localization and separation cues and hence it is more difficult to recognize the acoustic sources of interest. The objective of this research is to investigate signal processing and machine learning techniques that can be used for noise-robust acoustic target recognition using miniature microphone arrays.The first part of this research focuses on designing "smart" analog-to-digital conversion (ADC) algorithms that can enhance acoustic cues in sub-wavelength microphone arrays. Many source separation algorithms fail to deliver robust performance when applied to signals recorded using high-density sensor arrays where the distance between sensor elements is much less than the wavelength of the signals. This can be attributed to limited dynamic range (determined by analog-to-digital conversion) of the sensor which is insufficientto overcome the artifacts due to large cross-channel redundancy, non-homogeneous mixing and high-dimensionality of the signal space. We propose a novel framework that overcomes these limitations by integrating statistical learning directly with the signal measurement (analog-to-digital) process which enables high fidelity separation of linear instantaneous mixture. At the core of the proposed ADC approach is a min-max optimization of a regularized objective function that yields a sequence of quantized parameters which asymptotically tracks the statistics of the input signal. Experiments with synthetic and real recordings demonstrate consistent performance improvements when the proposed approach is used as the analog-to-digital front-end to conventional source separation algorithms.The second part of this research focuses on investigating a novel speech feature extraction algorithm that can recognize auditory targets (keywords and speakers) using noisy recordings. The features known as Sparse Auditory Reproducing Kernel (SPARK) coefficients are extracted under the hypothesis that the noise-robust information in speech signal is embedded in a subspace spanned by sparse, regularized, over-complete, non-linear, and phase-shifted gammatone basis functions. The feature extraction algorithm involves computing kernel functions between the speech data and pre-computed set of phased-shifted gammatone functions, followed by a simple pooling technique ("MAX" operation). In this work, we present experimental results for a hidden Markov model (HMM) based speech recognition system whose performance has been evaluated on a standard AURORA 2 dataset. The results demonstrate that the SPARK features deliver…

Thesis M.S. Michigan State University. Electrical Engineering 2012.

Secure and efficient communication between human being and managed devices is critical for Smart Grid and Smart Home. This thesis considers the architecture and design of a secure access gateway (SAG) for home area networks. The SAG serves as the interface between the remote users and the managed devices, such that real-time secure monitoring and control of the devices can be achieved through a Smart Phone. We try to address the security and capacity challenges using multilayer techniques. Security enhancement is ensured through network layer protocol development, as well as inherently secure physical layer transceiver design, capacity improvement is achieved through dynamic spectrum sharing. We also develop an evaluation platform of our proposed system and implement our secure algorithm on that platform. Remote monitoring and control of home/office devices through a Smart Phone is coming closer to us more than ever before.

Description based on online resource; title from PDF t.p. (ProQuest, viewed Sept. 20, 2013)

Thesis M.S. Michigan State University. Electrical Engineering 2011.

Analog circuits that use on-chip digital-to-analog converters for calibration use DSP based algorithms for optimizing and calibrating the system parameters. However, the performance of traditional online-gradient descent based optimization and calibration algorithms suffer from artifacts due to quantization noise which adversely affects the real-time and precise convergence to the desired parameters. This thesis proposes and analyzes a novel class of on-line learning algorithms that can noise-shape the effect of quantization noise during the adaptation procedure and in the process achieve faster spectral convergence compared to the conventional quantized gradient-descent approach. We extend the proposed framework to higher-order noise-shaping and derive criteria for achieving optimal system performance. The thesis also explores the application of stochastic perturbative gradient descent techniques to the proposed noise-shaping online learning framework where we show the performance of the stochastic algorithm can be improved in the spectral domain. The thesis applies the proposed optimization method for online calibration of subthreshold analog circuits where artifacts like mismatch and non-linearity are more pronounced. We also show that even with non-monotonic calibration DACs, the proposed algorithm is still able to find an optimal system solution without getting trapped into local minima. Using measured results obtained from prototype fabricated in a 0.5μm CMOS process, we demonstrate the robustness of the proposed algorithm for the task of: (a) compensating and tracking of offset parameters; and (b) calibration of the center frequency of a sub-threshold gm-C biquad filter.

Description based on online resource; title from PDF t.p. (ProQuest, viewed Feb. 25, 2013)

Thesis Ph. D. Michigan State University. Electrical Engineering 2014.

This dissertation presents a series of studies which explores the use of the structural phase transition of VO2 in MEMS actuators. MEMS actuators enable the interaction of devices with their surroundings, instead of simply sensing it. Actuation mechanisms, which are very common in the macro scale, such as pneumatics, induction motors, and combustion engines, are very difficult and inefficient to implement in the micro scale. Therefore, new actuation techniques must be developed and optimized. Current actuation mechanisms such as electrostatic or thermal expansion have limited performance in terms of total displacement, applied force, operating voltage, and power requirements. This has resulted in a push to incorporate new multifunctional smart materials into standard MEMS devices to improve performance. VO2 has proven to be one of the most promising materials for the creation of new sensors and actuators. Although most of its electrical and optical properties have been thoroughly studied, there is very little work related to its SPT and its application to MEMS actuators. Throughout this dissertation the SPT of VO2 is extensively studied, methods are developed for the optimal design of VO2-based actuators. The memory effect caused by the hysteresis in the SPT is exploited to create very robust programmable multiple state actuators. And fabrication processes are developed which combine the use of VO2 with standard MEMS fabrication processes.

Description based on online resource; title from PDF t.p. (viewed on May 30, 2017)

Thesis M.S. Michigan State University. Electrical Engineering 2011.

Understanding the structure and function of proteins has become increasingly important since the completion of the Human Genome Project and the sequencing of several other important genomes. In recent years, lab-on-a-chip systems have introduced some new capabilities for protein analyses. Rapid progress in the field of microsystems, miniaturized devices combining sensor and electronics, enable a new generation of miniaturized biosensor arrays integrating silicon CMOS chips that acquire and process bio-electrochemical signals. Such biosensor array microsystems could permit improved sensitivity, throughput and cost. Because many proteins exhibit temperature dependent activity, this thesis explores the opportunity to develop a thermal control microsystem for protein arrays biosensors. A CMOS microhotplate array was developed for thermoregulation of protein interfaces in a liquid sample environment. The microhotplates were shown to provide suitable thermal control for biosensor temperature ranges without the process complexity of most previously reported microhotplates. When combined with a CMOS analog thermal controller, the on-chip array was shown to set and hold temperatures for each protein site within ±1°C, and array elements were found to be almost completely thermally isolated from each other at distances beyond 0.4mm. Furthermore, a new compact, low power impedance analysis circuit was developed utilizing mixed-mode signal processing to extract real and imaginary impedance components for an on-chip protein interface. The compact size and low power of this circuit enable it to be combined with the thermal control structures and instantiated for every element in a sensor array to increase the interrogation throughput. The developed thermal control and readout microsystem could significantly advance proteomics research and progress in characterizing newly sequenced genomes.

Description based on online resource; title from PDF t.p. (viewed on Jan. 27, 2012)

Thesis Ph. D. Michigan State University. Electrical Engineering 2011.

Redox enzyme based electrochemical biosensors provide label-free continuous monitoring of biomolecules. This thesis work aims to solve the key challenges in constructing a microsystem that integrates enzyme-based electrochemical sensors, electrode arrays, CMOS instrumentation circuits, and microfluidics. A CMOS compatible enzyme immobilization technique based on conductive polymers is introduced and demonstrated through a biosensor based on an alcohol dehydrogenase enzyme. Utilizing a thorough study of the cross-disciplinary compatibility requirements for on-CMOS electrochemical sensors, a microfabricated electrode array scheme is identified and further optimized through a concentric ring working electrode design that minimizes electrode area and processing complexity. A new CMOS bipotentiostat architecture is introduced which, when used in conjunction with the concentric ring electrodes, implements an electrochemical interrogation scheme that enables signal amplification through the redox recycling with enzyme modified electrodes. Finally, a novel lab-on-CMOS integration platform is introduced that unites the capabilities of lab-on-chip microfluidic systems with the performance advantages of CMOS microsystems to integrated bios. This work establishes a miniaturized platform for integrating a variety of enzymes as biosensing elements that can be utilized to analyze biological samples using powerful electrochemical techniques. By integrating significant developments in enzyme immobilization, CMOS compatible microelectrode arrays, and CMOS instrumentation for redox recycling, this research advances the fields of point-of-care medical diagnostics, high-throughput screening, and a wide range of additional sensing applications.

Thesis Ph. D. Michigan State University. Computer Science 2013.

Prostate cancer is one of the most common and dreadful type of cancer in men. Due to the unclear symptoms of the disease, the diagnosis of prostate cancer is difficult, and requires multiple procedures. Among these procedures, the most important one is the examination of the prostate tissue biopsy to detect the presence of cancer regions in the tissue, and assign a Gleason score to the tissue (which determines the severity of the cancer). The detection and grading processes are based on the glandular structures as well as the cytological properties of the tissue. In a traditional examination, pathologists have to look at the tissue biopsy under a microscope. With the developments in digital pathology, especially in virtual microscopy, glass tissue slides can be digitized to generate tissue images. These images can be displayed on a monitor, annotated by software tools, and forwarded to experts for examination and diagnosis. However, the large volume of tissue images that are generated poses a challenge for pathologists to efficiently and accurately perform the diagnosis. Hence, there is a need to develop tools for automatic processing of prostate tissue images, which can assist pathologists in decision making and improve the throughput. This thesis deals with design and development of automatic tools for processing and analyzing prostate tissue images.In tissue examination, the grading of tissue slides is a standard procedure to determine the severity of cancer. The most popular grading method is the Gleason grading, which relies on the gland structures in the tissue to assign a Gleason score ranging from 2 to 10 to the tissue image. We utilize the Gleason grading method in automated systems by segmenting glands from the tissue image and extracting features to discriminate them. By thorough analyses and evaluations, we demonstrate that the proposed methods lead to better gland classification accuracies than published methods in the literature.We utilize the proposed gland segmentation and gland feature extraction methods to solve the tissue image classification problem, which receives the most attraction in the literature. By comparing with popular texture-based methods, we show that using the proposed gland features is a better solution for this problem. To further improve the Gleason grade 3 vs Gleason grade 4 classification result, we propose a different approach for gland segmentation and study the properties of the nuclei arrangement in the segmented glands.When a medical laboratory technician or a medical student who is not very experienced with Gleason grading wants to gain additional experience in the grading process, it will be useful if there was an image retrieval engine that could search for tissue regions similar to the region of interest (ROI) in the tissue slides that were annotated by experienced pathologists. The technician (or student) can use the retrieved regions (whose Gleason grades are known) as the references to grade the ROI.…

Thesis Ph. D. Michigan State University. Electrical Engineering 2011.

Magnetic molecular-level sensing and manipulation are emerging as lab-on-chip platforms. These platforms entail low-cost, low-power, high efficiency, and portable implementations. Biomaterials are usually attached to magnetic beads and used in bio-analysis applications such as sorting, counting, purification, and assembly. Some of the potential applications are 2D biological or artificial tissue assembly at the micro-scale level.Here, we present the design and demonstration of a self-contained device for sensing and manipulating biomaterials tagged with magnetic beads. The core elements of the device consist of all-integrated programmable magnetic coil arrays for pseudo-parallel sensing and actuation, which are capable of maneuvering small (bead-bound) bio-objects individually and larger ones collaboratively. Our design does not require any external magnetic sources. It relies on the magnetic field generated by planar on-chip coil arrays. The coil arrays are selectively and dynamically controlled. Each element, composed of the coil and its logical control circuitry, can detect bio-objects in the order of 1μm diameter, or manipulate them using eight-level programmable AC or DC magnetic fields. All array sensing and actuation components are shared and multiplexed to reduce the overall imprint. The components are isolated and tuned to work at 900MHz by incorporating high-speed switching (up to 40MHz) for seamless pseudo-parallel execution.In addition, we present a new and unique on-chip biomedical proof-of-concept application. Adopting trends in neuroscience, we employ the magnetic beads to initiate and elongate neuronal axons in vitro on-chip. This application domain will assist in better understanding and studying the process of neuroregeneration, propel future clinical applications, and provide the foundation of techniques needed to wire ``neuronal circuits'' using living cells.

Description based on online resource; title from PDF t.p. (ProQuest, viewed Jan. 10, 2013)

Thesis Ph. D. Michigan State University. Electrical Engineering 2014.

Structural health monitoring (SHM) has the potential to significantly increase safety and reduce manufacturing and maintenance costs of industrial structures. The use of piezoelectric material such as Lead-Zirconate-Titanate (PZT) in exciting and sensing ultrasonic guidedwaves for damage detection has become popular since its allows the rapid inspection of large areas in a structure using non-intrusive sensors. Ultrasonic guided waves interact with discontinuities in the structure, giving information about the potential presence of a damage, its size and location.The main concerns about using such methods is that PZT sensors and guided waves are affected by environmental conditions. The performance of the PZT sensors, in terms of their ability to detect damage, degrades over time and varies depending on current environmental conditions and surrounding, resulting in inconsistent measurements. This work gives a novel formulation of a model-based probability of detection method, which is able to quantifythe performance of guided wave inspection in a stochastically varying environment. An analytically and experimentally verified finite element model is used to generate data that represent the effects of varying environmental conditions. Then the stochastic approachis used to evaluate the probability of detection of cracks in riveted aluminum plates and delaminations in composite plates. Also, the performance of guided wave imaging algorithms under degrading PZT conditions is examined.Another concern is the ability to transfer data from the PZT sensors, which are per-manently located on the structure, to a computer where the data could be processed in real-time or near real-time. Wireless sensor networks (WSN) use low footprint smart sensor nodes that are permanently mounted on the structure. The sensor nodes have their own power supply and wireless communication devices to communicate with other sensor nodes or a base station. Wireless sensor node for guided waves require an actuation interfacesand high frequency sampling of the guided wave measurements. A proof-of-concept wireless sensor node prototype is developed for the data acquisition and actuation using PZT sensorsand guided waves.

Description based on online resource; title from PDF t.p. (viewed on May 17, 2017)

Thesis Ph. D. Michigan State University. Biosystems Engineering 2014.

Escherichia coli O157:H7 is one of the main foodborne/waterborne bacterial pathogens that can cause human illnesses with threat to public health. To control the spread of the contaminated food/water and minimize the harm to public health, rapid and sensitive detection methods need to be implemented. However, standard culture method requires two to four days to obtain results. The application of nanomaterials has drawn interest in the biosensor research to develop timely and low cost detection systems. Because of their unique characteristics, nanoparticles have been used to enhance biosensor sensitivity by increasing the target molecule capture efficiency or by amplifying detection signals. In this dissertation research, nanoparticle-based biosensors were designed for the rapid detection of E. coli O157:H7 in broth. Magnetic nanoparticles (MNPs) were conjugated with monoclonal antibodies to separate target E. coli O157:H7 cells from samples. Gold nanoparticles (AuNPs) conjugated with polyclonal antibodies were then introduced to the MNP-target complexes to form MNP-target-AuNP. By measuring the amount of gold nanoparticles through an electrochemical method, the presence and the amount of the target bacteria were determined. Based on this biosensor using AuNPs as labels for signal amplification, a tri-nano electrochemical immunosensor was developed by using three nanoparticles for the rapid detection. The gold nanoparticles (AuNPs) were conjugated with lead sulfide (PbS) nanoparticles as electrochemical reporters via oligonucleotide linkage. AuNPs were also functionalized with polyclonal anti-E. coli O157:H7 antibodies in order to bind the target bacterial cells which were captured and separated from the sample by antibody-functionalized MNPs. Because each AuNP was linked to multiple PbS nanoparticles, each binding event to the target resulted in substantial amplification. The signal of PbS was measured on screen-printed carbon electrode (SPCE) by square wave anodic stripping voltammetry (SWASV). Results showed that the biosensor could detect E. coli O157:H7 in the range of 10¹ to 10⁶ colony forming units per milliliter (cfu/ml) with a signal-to-noise ratio ranging from 2.77 to 4.31. With sample preparation being minimized, results were obtained within 1 h from sample processing to final readout. Tuberculosis (TB) is considered as one of the most widely spread infectious diseases, with estimated 8.8 million new cases and 2 million deaths annually. The biosensor developed in this dissertation research was also applied for TB diagnosis. Gold nanoparticles with anti-IFN-gamma antibody were conjugated to oligonucleotides terminated with cadmium sulfide (CdS) nanoparticles. At the same time, AuNPs were conjugated with anti-IP-10 antibody and oligonucleotides terminated with PbS nanoparticles. Therefore, the electrochemical signals of…

Thesis Ph. D. Michigan State University. Electrical Engineering 2016

"The objective of this dissertation is to investigate the power conversion efficiency and the power threshold of energy harvesting technologies, and then propose several solutions to overcome these issues."  – Abstract.

Description based on online resource;