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- All Subjects: Electrical Engineering
- Genre: Academic theses
- Creators: Papandreou-Suppappola, Antonia
- Creators: Kitchen, Jennifer
Increasing the efficiency of Doppler processing and backend processing in medical ultrasound systems
Description
Ultrasound imaging is one of the major medical imaging modalities. It is cheap, non-invasive and has low power consumption. Doppler processing is an important part of many ultrasound imaging systems. It is used to provide blood velocity information and is built on top of B-mode systems. We investigate the performance of two velocity estimation schemes used in Doppler processing systems, namely, directional velocity estimation (DVE) and conventional velocity estimation (CVE). We find that DVE provides better estimation performance and is the only functioning method when the beam to flow angle is large. Unfortunately, DVE is computationally expensive and also requires divisions and square root operations that are hard to implement. We propose two approximation techniques to replace these computations. The simulation results on cyst images show that the proposed approximations do not affect the estimation performance. We also study backend processing which includes envelope detection, log compression and scan conversion. Three different envelope detection methods are compared. Among them, FIR based Hilbert Transform is considered the best choice when phase information is not needed, while quadrature demodulation is a better choice if phase information is necessary. Bilinear and Gaussian interpolation are considered for scan conversion. Through simulations of a cyst image, we show that bilinear interpolation provides comparable contrast-to-noise ratio (CNR) performance with Gaussian interpolation and has lower computational complexity. Thus, bilinear interpolation is chosen for our system.
ContributorsWei, Siyuan (Author) / Chakrabarti, Chaitali (Thesis advisor) / Frakes, David (Committee member) / Papandreou-Suppappola, Antonia (Committee member) / Arizona State University (Publisher)
Created2013
Description
Radio frequency (RF) transceivers require a disproportionately high effort in terms of test development time, test equipment cost, and test time. The relatively high test cost stems from two contributing factors. First, RF transceivers require the measurement of a diverse set of specifications, requiring multiple test set-ups and long test times, which complicates load-board design, debug, and diagnosis. Second, high frequency operation necessitates the use of expensive equipment, resulting in higher per second test time cost compared with mixed-signal or digital circuits. Moreover, in terms of the non-recurring engineering cost, the need to measure complex specfications complicates the test development process and necessitates a long learning process for test engineers. Test time is dominated by changing and settling time for each test set-up. Thus, single set-up test solutions are desirable. Loop-back configuration where the transmitter output is connected to the receiver input are used as the desirable test set- up for RF transceivers, since it eliminates the reliance on expensive instrumentation for RF signal analysis and enables measuring multiple parameters at once. In-phase and Quadrature (IQ) imbalance, non-linearity, DC offset and IQ time skews are some of the most detrimental imperfections in transceiver performance. Measurement of these parameters in the loop-back mode is challenging due to the coupling between the receiver (RX) and transmitter (TX) parameters. Loop-back based solutions are proposed in this work to resolve this issue. A calibration algorithm for a subset of the above mentioned impairments is also presented. Error Vector Magnitude (EVM) is a system-level parameter that is specified for most advanced communication standards. EVM measurement often takes extensive test development efforts, tester resources, and long test times. EVM is analytically related to system impairments, which are typically measured in a production test i environment. Thus, EVM test can be eliminated from the test list if the relations between EVM and system impairments are derived independent of the circuit implementation and manufacturing process. In this work, the focus is on the WLAN standard, and deriving the relations between EVM and three of the most detrimental impairments for QAM/OFDM based systems (IQ imbalance, non-linearity, and noise). Having low cost test techniques for measuring the RF transceivers imperfections and being able to analytically compute EVM from the measured parameters is a complete test solution for RF transceivers. These techniques along with the proposed calibration method can be used in improving the yield by widening the pass/fail boundaries for transceivers imperfections. For all of the proposed methods, simulation and hardware measurements prove that the proposed techniques provide accurate characterization of RF transceivers.
ContributorsNassery, Afsaneh (Author) / Ozev, Sule (Thesis advisor) / Bakkaloglu, Bertan (Committee member) / Kiaei, Sayfe (Committee member) / Kitchen, Jennifer (Committee member) / Arizona State University (Publisher)
Created2013
Description
Synchronous buck converters have become the obvious choice of design for high efficiency voltage down-conversion applications and find wide scale usage in today's IC industry. The use of digital control in synchronous buck converters is becoming increasingly popular because of its associated advantages over traditional analog counterparts in terms of design flexibility, reduced use of off-chip components, and better programmability to enable advanced controls. They also demonstrate better immunity to noise, enhances tolerance to the process, voltage and temperature (PVT) variations, low chip area and as a result low cost. It enables processing in digital domain requiring a need of analog-digital interfacing circuit viz. Analog to Digital Converter (ADC) and Digital to Analog Converter (DAC). A Digital to Pulse Width Modulator (DPWM) acts as time domain DAC required in the control loop to modulate the ON time of the Power-MOSFETs. The accuracy and efficiency of the DPWM creates the upper limit to the steady state voltage ripple of the DC - DC converter and efficiency in low load conditions. This thesis discusses the prevalent architectures for DPWM in switched mode DC - DC converters. The design of a Hybrid DPWM is presented. The DPWM is 9-bit accurate and is targeted for a Synchronous Buck Converter with a switching frequency of 1.0 MHz. The design supports low power mode(s) for the buck converter in the Pulse Frequency Modulation (PFM) mode as well as other fail-safe features. The design implementation is digital centric making it robust across PVT variations and portable to lower technology nodes. Key target of the design is to reduce design time. The design is tested across large Process (+/- 3σ), Voltage (1.8V +/- 10%) and Temperature (-55.0 °C to 125 °C) and is in the process of tape-out.
ContributorsKumar, Amit (Author) / Bakkaloglu, Bertan (Thesis advisor) / Song, Hongjiang (Committee member) / Kitchen, Jennifer (Committee member) / Arizona State University (Publisher)
Created2013
Description
Doppler radar can be used to measure respiration and heart rate without contact and through obstacles. In this work, a Doppler radar architecture at 2.4 GHz and a new signal processing algorithm to estimate the respiration and heart rate are presented. The received signal is dominated by the transceiver noise, LO phase noise and clutter which reduces the signal-to-noise ratio of the desired signal. The proposed architecture and algorithm are used to mitigate these issues and obtain an accurate estimate of the heart and respiration rate. Quadrature low-IF transceiver architecture is adopted to resolve null point problem as well as avoid 1/f noise and DC offset due to mixer-LO coupling. Adaptive clutter cancellation algorithm is used to enhance receiver sensitivity coupled with a novel Pattern Search in Noise Subspace (PSNS) algorithm is used to estimate respiration and heart rate. PSNS is a modified MUSIC algorithm which uses the phase noise to enhance Doppler shift detection. A prototype system was implemented using off-the-shelf TI and RFMD transceiver and tests were conduct with eight individuals. The measured results shows accurate estimate of the cardio pulmonary signals in low-SNR conditions and have been tested up to a distance of 6 meters.
ContributorsKhunti, Hitesh Devshi (Author) / Kiaei, Sayfe (Thesis advisor) / Bakkaloglu, Bertan (Committee member) / Bliss, Daniel (Committee member) / Kitchen, Jennifer (Committee member) / Arizona State University (Publisher)
Created2013
Description
This thesis work mainly examined the stability and reliability issues of amorphous Indium Gallium Zinc Oxide (a-IGZO) thin film transistors under bias-illumination stress. Amorphous hydrogenated silicon has been the dominating material used in thin film transistors as a channel layer. However with the advent of modern high performance display technologies, it is required to have devices with better current carrying capability and better reproducibility. This brings the idea of new material for channel layer of these devices. Researchers have tried poly silicon materials, organic materials and amorphous mixed oxide materials as a replacement to conventional amorphous silicon layer. Due to its low price and easy manufacturing process, amorphous mixed oxide thin film transistors have become a viable option to replace the conventional ones in order to achieve high performance display circuits. But with new materials emerging, comes the challenge of reliability and stability issues associated with it. Performance measurement under bias stress and bias-illumination stress have been reported previously. This work proposes novel post processing low temperature long time annealing in optimum ambient in order to annihilate or reduce the defects and vacancies associated with amorphous material which lead to the instability or even the failure of the devices. Thin film transistors of a-IGZO has been tested for standalone illumination stress and bias-illumination stress before and after annealing. HP 4155B semiconductor parameter analyzer has been used to stress the devices and measure the output characteristics and transfer characteristics of the devices. Extra attention has been given about the effect of forming gas annealing on a-IGZO thin film. a-IGZO thin film deposited on silicon substrate has been tested for resistivity, mobility and carrier concentration before and after annealing in various ambient. Elastic Recoil Detection has been performed on the films to measure the amount of hydrogen atoms present in the film. Moreover, the circuit parameters of the thin film transistors has been extracted to verify the physical phenomenon responsible for the instability and failure of the devices. Parameters like channel resistance, carrier mobility, power factor has been extracted and variation of these parameters has been observed before and after the stress.
ContributorsRuhul Hasin, Muhammad (Author) / Alford, Terry L. (Thesis advisor) / Krause, Stephen (Committee member) / Kitchen, Jennifer (Committee member) / Arizona State University (Publisher)
Created2013
Description
Distributed inference has applications in a wide range of fields such as source localization, target detection, environment monitoring, and healthcare. In this dissertation, distributed inference schemes which use bounded transmit power are considered. The performance of the proposed schemes are studied for a variety of inference problems. In the first part of the dissertation, a distributed detection scheme where the sensors transmit with constant modulus signals over a Gaussian multiple access channel is considered. The deflection coefficient of the proposed scheme is shown to depend on the characteristic function of the sensing noise, and the error exponent for the system is derived using large deviation theory. Optimization of the deflection coefficient and error exponent are considered with respect to a transmission phase parameter for a variety of sensing noise distributions including impulsive ones. The proposed scheme is also favorably compared with existing amplify-and-forward (AF) and detect-and-forward (DF) schemes. The effect of fading is shown to be detrimental to the detection performance and simulations are provided to corroborate the analytical results. The second part of the dissertation studies a distributed inference scheme which uses bounded transmission functions over a Gaussian multiple access channel. The conditions on the transmission functions under which consistent estimation and reliable detection are possible is characterized. For the distributed estimation problem, an estimation scheme that uses bounded transmission functions is proved to be strongly consistent provided that the variance of the noise samples are bounded and that the transmission function is one-to-one. The proposed estimation scheme is compared with the amplify and forward technique and its robustness to impulsive sensing noise distributions is highlighted. It is also shown that bounded transmissions suffer from inconsistent estimates if the sensing noise variance goes to infinity. For the distributed detection problem, similar results are obtained by studying the deflection coefficient. Simulations corroborate our analytical results. In the third part of this dissertation, the problem of estimating the average of samples distributed at the nodes of a sensor network is considered. A distributed average consensus algorithm in which every sensor transmits with bounded peak power is proposed. In the presence of communication noise, it is shown that the nodes reach consensus asymptotically to a finite random variable whose expectation is the desired sample average of the initial observations with a variance that depends on the step size of the algorithm and the variance of the communication noise. The asymptotic performance is characterized by deriving the asymptotic covariance matrix using results from stochastic approximation theory. It is shown that using bounded transmissions results in slower convergence compared to the linear consensus algorithm based on the Laplacian heuristic. Simulations corroborate our analytical findings. Finally, a robust distributed average consensus algorithm in which every sensor performs a nonlinear processing at the receiver is proposed. It is shown that non-linearity at the receiver nodes makes the algorithm robust to a wide range of channel noise distributions including the impulsive ones. It is shown that the nodes reach consensus asymptotically and similar results are obtained as in the case of transmit non-linearity. Simulations corroborate our analytical findings and highlight the robustness of the proposed algorithm.
ContributorsDasarathan, Sivaraman (Author) / Tepedelenlioğlu, Cihan (Thesis advisor) / Papandreou-Suppappola, Antonia (Committee member) / Reisslein, Martin (Committee member) / Goryll, Michael (Committee member) / Arizona State University (Publisher)
Created2013
Description
Electrical neural activity detection and tracking have many applications in medical research and brain computer interface technologies. In this thesis, we focus on the development of advanced signal processing algorithms to track neural activity and on the mapping of these algorithms onto hardware to enable real-time tracking. At the heart of these algorithms is particle filtering (PF), a sequential Monte Carlo technique used to estimate the unknown parameters of dynamic systems. First, we analyze the bottlenecks in existing PF algorithms, and we propose a new parallel PF (PPF) algorithm based on the independent Metropolis-Hastings (IMH) algorithm. We show that the proposed PPF-IMH algorithm improves the root mean-squared error (RMSE) estimation performance, and we demonstrate that a parallel implementation of the algorithm results in significant reduction in inter-processor communication. We apply our implementation on a Xilinx Virtex-5 field programmable gate array (FPGA) platform to demonstrate that, for a one-dimensional problem, the PPF-IMH architecture with four processing elements and 1,000 particles can process input samples at 170 kHz by using less than 5% FPGA resources. We also apply the proposed PPF-IMH to waveform-agile sensing to achieve real-time tracking of dynamic targets with high RMSE tracking performance. We next integrate the PPF-IMH algorithm to track the dynamic parameters in neural sensing when the number of neural dipole sources is known. We analyze the computational complexity of a PF based method and propose the use of multiple particle filtering (MPF) to reduce the complexity. We demonstrate the improved performance of MPF using numerical simulations with both synthetic and real data. We also propose an FPGA implementation of the MPF algorithm and show that the implementation supports real-time tracking. For the more realistic scenario of automatically estimating an unknown number of time-varying neural dipole sources, we propose a new approach based on the probability hypothesis density filtering (PHDF) algorithm. The PHDF is implemented using particle filtering (PF-PHDF), and it is applied in a closed-loop to first estimate the number of dipole sources and then their corresponding amplitude, location and orientation parameters. We demonstrate the improved tracking performance of the proposed PF-PHDF algorithm and map it onto a Xilinx Virtex-5 FPGA platform to show its real-time implementation potential. Finally, we propose the use of sensor scheduling and compressive sensing techniques to reduce the number of active sensors, and thus overall power consumption, of electroencephalography (EEG) systems. We propose an efficient sensor scheduling algorithm which adaptively configures EEG sensors at each measurement time interval to reduce the number of sensors needed for accurate tracking. We combine the sensor scheduling method with PF-PHDF and implement the system on an FPGA platform to achieve real-time tracking. We also investigate the sparsity of EEG signals and integrate compressive sensing with PF to estimate neural activity. Simulation results show that both sensor scheduling and compressive sensing based methods achieve comparable tracking performance with significantly reduced number of sensors.
ContributorsMiao, Lifeng (Author) / Chakrabarti, Chaitali (Thesis advisor) / Papandreou-Suppappola, Antonia (Thesis advisor) / Zhang, Junshan (Committee member) / Bliss, Daniel (Committee member) / Kovvali, Narayan (Committee member) / Arizona State University (Publisher)
Created2013
Description
A signal with time-varying frequency content can often be expressed more clearly using a time-frequency representation (TFR), which maps the signal into a two-dimensional function of time and frequency, similar to musical notation. The thesis reviews one of the most commonly used TFRs, the Wigner distribution (WD), and discusses its application in Fourier optics: it is shown that the WD is analogous to the spectral dispersion that results from a diffraction grating, and time and frequency are similarly analogous to a one dimensional spatial coordinate and wavenumber. The grating is compared with a simple polychromator, which is a bank of optical filters. Another well-known TFR is the short time Fourier transform (STFT). Its discrete version can be shown to be equivalent to a filter bank, an array of bandpass filters that enable localized processing of the analysis signals in different sub-bands. This work proposes a signal-adaptive method of generating TFRs. In order to minimize distortion in analyzing a signal, the method modifies the filter bank to consist of non-overlapping rectangular bandpass filters generated using the Butterworth filter design process. The information contained in the resulting TFR can be used to reconstruct the signal, and perfect reconstruction techniques involving quadrature mirror filter banks are compared with a simple Fourier synthesis sum. The optimal filter parameters of the rectangular filters are selected adaptively by minimizing the mean-squared error (MSE) from a pseudo-reconstructed version of the analysis signal. The reconstruction MSE is proposed as an error metric for characterizing TFRs; a practical measure of the error requires normalization and cross correlation with the analysis signal. Simulations were performed to demonstrate the the effectiveness of the new adaptive TFR and its relation to swept-tuned spectrum analyzers.
ContributorsWeber, Peter C. (Author) / Papandreou-Suppappola, Antonia (Thesis advisor) / Tepedelenlioğlu, Cihan (Committee member) / Kovvali, Narayan (Committee member) / Arizona State University (Publisher)
Created2012
Description
Adaptive processing and classification of electrocardiogram (ECG) signals are important in eliminating the strenuous process of manually annotating ECG recordings for clinical use. Such algorithms require robust models whose parameters can adequately describe the ECG signals. Although different dynamic statistical models describing ECG signals currently exist, they depend considerably on a priori information and user-specified model parameters. Also, ECG beat morphologies, which vary greatly across patients and disease states, cannot be uniquely characterized by a single model. In this work, sequential Bayesian based methods are used to appropriately model and adaptively select the corresponding model parameters of ECG signals. An adaptive framework based on a sequential Bayesian tracking method is proposed to adaptively select the cardiac parameters that minimize the estimation error, thus precluding the need for pre-processing. Simulations using real ECG data from the online Physionet database demonstrate the improvement in performance of the proposed algorithm in accurately estimating critical heart disease parameters. In addition, two new approaches to ECG modeling are presented using the interacting multiple model and the sequential Markov chain Monte Carlo technique with adaptive model selection. Both these methods can adaptively choose between different models for various ECG beat morphologies without requiring prior ECG information, as demonstrated by using real ECG signals. A supervised Bayesian maximum-likelihood (ML) based classifier uses the estimated model parameters to classify different types of cardiac arrhythmias. However, the non-availability of sufficient amounts of representative training data and the large inter-patient variability pose a challenge to the existing supervised learning algorithms, resulting in a poor classification performance. In addition, recently developed unsupervised learning methods require a priori knowledge on the number of diseases to cluster the ECG data, which often evolves over time. In order to address these issues, an adaptive learning ECG classification method that uses Dirichlet process Gaussian mixture models is proposed. This approach does not place any restriction on the number of disease classes, nor does it require any training data. This algorithm is adapted to be patient-specific by labeling or identifying the generated mixtures using the Bayesian ML method, assuming the availability of labeled training data.
ContributorsEdla, Shwetha Reddy (Author) / Papandreou-Suppappola, Antonia (Thesis advisor) / Chakrabarti, Chaitali (Committee member) / Kovvali, Narayan (Committee member) / Tepedelenlioğlu, Cihan (Committee member) / Arizona State University (Publisher)
Created2012
Description
Immunosignaturing is a medical test for assessing the health status of a patient by applying microarrays of random sequence peptides to determine the patient's immune fingerprint by associating antibodies from a biological sample to immune responses. The immunosignature measurements can potentially provide pre-symptomatic diagnosis for infectious diseases or detection of biological threats. Currently, traditional bioinformatics tools, such as data mining classification algorithms, are used to process the large amount of peptide microarray data. However, these methods generally require training data and do not adapt to changing immune conditions or additional patient information. This work proposes advanced processing techniques to improve the classification and identification of single and multiple underlying immune response states embedded in immunosignatures, making it possible to detect both known and previously unknown diseases or biothreat agents. Novel adaptive learning methodologies for un- supervised and semi-supervised clustering integrated with immunosignature feature extraction approaches are proposed. The techniques are based on extracting novel stochastic features from microarray binding intensities and use Dirichlet process Gaussian mixture models to adaptively cluster the immunosignatures in the feature space. This learning-while-clustering approach allows continuous discovery of antibody activity by adaptively detecting new disease states, with limited a priori disease or patient information. A beta process factor analysis model to determine underlying patient immune responses is also proposed to further improve the adaptive clustering performance by formatting new relationships between patients and antibody activity. In order to extend the clustering methods for diagnosing multiple states in a patient, the adaptive hierarchical Dirichlet process is integrated with modified beta process factor analysis latent feature modeling to identify relationships between patients and infectious agents. The use of Bayesian nonparametric adaptive learning techniques allows for further clustering if additional patient data is received. Significant improvements in feature identification and immune response clustering are demonstrated using samples from patients with different diseases.
ContributorsMalin, Anna (Author) / Papandreou-Suppappola, Antonia (Thesis advisor) / Bliss, Daniel (Committee member) / Chakrabarti, Chaitali (Committee member) / Kovvali, Narayan (Committee member) / Lacroix, Zoé (Committee member) / Arizona State University (Publisher)
Created2013