Matching Items (118)
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- All Subjects: Electrical Engineering
- Creators: Vasileska, Dragica
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
Solar energy, including solar heating, solar architecture, solar thermal electricity and solar photovoltaics, is one of the primary energy sources replacing fossil fuels. Being one of the most important techniques, significant research has been conducted in solar cell efficiency improvement. Simulation of various structures and materials of solar cells provides a deeper understanding of device operation and ways to improve their efficiency. Over the last two decades, polycrystalline thin-film Cadmium-Sulfide and Cadmium-Telluride (CdS/CdTe) solar cells fabricated on glass substrates have been considered as one of the most promising candidate in the photovoltaic technologies, for their similar efficiency and low costs when compared to traditional silicon-based solar cells. In this work a fast one dimensional time-dependent/steady-state drift-diffusion simulator, accelerated by adaptive non-uniform mesh and automatic time-step control, for modeling solar cells has been developed and has been used to simulate a CdS/CdTe solar cell. These models are used to reproduce transients of carrier transport in response to step-function signals of different bias and varied light intensity. The time-step control models are also used to help convergence in steady-state simulations where constrained material constants, such as carrier lifetimes in the order of nanosecond and carrier mobility in the order of 100 cm2/Vs, must be applied.
ContributorsGuo, Da (Author) / Vasileska, Dragica (Thesis advisor) / Goodnick, Stephen M (Committee member) / Sankin, Igor (Committee member) / Arizona State University (Publisher)
Created2013
Description
Digital sound synthesis allows the creation of a great variety of sounds. Focusing on interesting or ecologically valid sounds for music, simulation, aesthetics, or other purposes limits the otherwise vast digital audio palette. Tools for creating such sounds vary from arbitrary methods of altering recordings to precise simulations of vibrating objects. In this work, methods of sound synthesis by re-sonification are considered. Re-sonification, herein, refers to the general process of analyzing, possibly transforming, and resynthesizing or reusing recorded sounds in meaningful ways, to convey information. Applied to soundscapes, re-sonification is presented as a means of conveying activity within an environment. Applied to the sounds of objects, this work examines modeling the perception of objects as well as their physical properties and the ability to simulate interactive events with such objects. To create soundscapes to re-sonify geographic environments, a method of automated soundscape design is presented. Using recorded sounds that are classified based on acoustic, social, semantic, and geographic information, this method produces stochastically generated soundscapes to re-sonify selected geographic areas. Drawing on prior knowledge, local sounds and those deemed similar comprise a locale's soundscape. In the context of re-sonifying events, this work examines processes for modeling and estimating the excitations of sounding objects. These include plucking, striking, rubbing, and any interaction that imparts energy into a system, affecting the resultant sound. A method of estimating a linear system's input, constrained to a signal-subspace, is presented and applied toward improving the estimation of percussive excitations for re-sonification. To work toward robust recording-based modeling and re-sonification of objects, new implementations of banded waveguide (BWG) models are proposed for object modeling and sound synthesis. Previous implementations of BWGs use arbitrary model parameters and may produce a range of simulations that do not match digital waveguide or modal models of the same design. Subject to linear excitations, some models proposed here behave identically to other equivalently designed physical models. Under nonlinear interactions, such as bowing, many of the proposed implementations exhibit improvements in the attack characteristics of synthesized sounds.
ContributorsFink, Alex M (Author) / Spanias, Andreas S (Thesis advisor) / Cook, Perry R. (Committee member) / Turaga, Pavan (Committee member) / Tsakalis, Konstantinos (Committee member) / Arizona State University (Publisher)
Created2013
Description
Magnetic Resonance Imaging using spiral trajectories has many advantages in speed, efficiency in data-acquistion and robustness to motion and flow related artifacts. The increase in sampling speed, however, requires high performance of the gradient system. Hardware inaccuracies from system delays and eddy currents can cause spatial and temporal distortions in the encoding gradient waveforms. This causes sampling discrepancies between the actual and the ideal k-space trajectory. Reconstruction assuming an ideal trajectory can result in shading and blurring artifacts in spiral images. Current methods to estimate such hardware errors require many modifications to the pulse sequence, phantom measurements or specialized hardware. This work presents a new method to estimate time-varying system delays for spiral-based trajectories. It requires a minor modification of a conventional stack-of-spirals sequence and analyzes data collected on three orthogonal cylinders. The method is fast, robust to off-resonance effects, requires no phantom measurements or specialized hardware and estimate variable system delays for the three gradient channels over the data-sampling period. The initial results are presented for acquired phantom and in-vivo data, which show a substantial reduction in the artifacts and improvement in the image quality.
ContributorsBhavsar, Payal (Author) / Pipe, James G (Thesis advisor) / Frakes, David (Committee member) / Kodibagkar, Vikram (Committee member) / Arizona State University (Publisher)
Created2013
Description
GaN high electron mobility transistors (HEMTs) based on the III-V nitride material system have been under extensive investigation because of their superb performance as high power RF devices. Two dimensional electron gas(2-DEG) with charge density ten times higher than that of GaAs-based HEMT and mobility much higher than Si enables a low on-resistance required for RF devices. Self-heating issues with GaN HEMT and lack of understanding of various phenomena are hindering their widespread commercial development. There is a need to understand device operation by developing a model which could be used to optimize electrical and thermal characteristics of GaN HEMT design for high power and high frequency operation. In this thesis work a physical simulation model of AlGaN/GaN HEMT is developed using commercially available software ATLAS from SILVACO Int. based on the energy balance/hydrodynamic carrier transport equations. The model is calibrated against experimental data. Transfer and output characteristics are the key focus in the analysis along with saturation drain current. The resultant IV curves showed a close correspondence with experimental results. Various combinations of electron mobility, velocity saturation, momentum and energy relaxation times and gate work functions were attempted to improve IV curve correlation. Thermal effects were also investigated to get a better understanding on the role of self-heating effects on the electrical characteristics of GaN HEMTs. The temperature profiles across the device were observed. Hot spots were found along the channel in the gate-drain spacing. These preliminary results indicate that the thermal effects do have an impact on the electrical device characteristics at large biases even though the amount of self-heating is underestimated with respect to thermal particle-based simulations that solve the energy balance equations for acoustic and optical phonons as well (thus take proper account of the formation of the hot-spot). The decrease in drain current is due to decrease in saturation carrier velocity. The necessity of including hydrodynamic/energy balance transport models for accurate simulations is demonstrated. Possible ways for improving model accuracy are discussed in conjunction with future research.
ContributorsChowdhury, Towhid (Author) / Vasileska, Dragica (Thesis advisor) / Goodnick, Stephen (Committee member) / Goryll, Michael (Committee member) / Arizona State University (Publisher)
Created2013
Description
Since its inception about three decades ago, silicon on insulator (SOI) technology has come a long way to be included in the microelectronics roadmap. Earlier, scientists and engineers focused on ways to increase the microprocessor clock frequency and speed. Today, with smart phones and tablets gaining popularity, power consumption has become a major factor. In this thesis, self-heating effects in a 25nm fully depleted (FD) SOI device are studied by implementing a 2-D particle based device simulator coupled self-consistently with the energy balance equations for both acoustic and optical phonons. Semi-analytical expressions for acoustic and optical phonon scattering rates (all modes) are derived and evaluated using quadratic dispersion relationships. Moreover, probability distribution functions for the final polar angle after scattering is also computed and the rejection technique is implemented for its selection. Since the temperature profile varies throughout the device, temperature dependent scattering tables are used for the electron transport kernel. The phonon energy balance equations are also modified to account for inelasticity in acoustic phonon scattering for all branches. Results obtained from this simulation help in understanding self-heating and the effects it has on the device characteristics. The temperature profiles in the device show a decreasing trend which can be attributed to the inelastic interaction between the electrons and the acoustic phonons. This is further proven by comparing the temperature plots with the simulation results that assume the elastic and equipartition approximation for acoustic and the Einstein model for optical phonons. Thus, acoustic phonon inelasticity and the quadratic phonon dispersion relationships play a crucial role in studying self-heating effects.
ContributorsGada, Manan Laxmichand (Author) / Vasileska, Dragica (Thesis advisor) / Ferry, David K. (Committee member) / Goodnick, Stephen M (Committee member) / Arizona State University (Publisher)
Created2013
Description
In this thesis, quantitative evaluation of quality of movement during stroke rehabilitation will be discussed. Previous research on stroke rehabilitation in hospital has been shown to be effective. In this thesis, we study various issues that arise when creating a home-based system that can be deployed in a patient's home. Limitation of motion capture due to reduced number of sensors leads to problems with design of kinematic features for quantitative evaluation. Also, the hierarchical three-level tasks of rehabilitation requires new design of kinematic features. In this thesis, the design of kinematic features for a home based stroke rehabilitation system will be presented. Results of the most challenging classifier are shown and proves the effectiveness of the design. Comparison between modern classification techniques and low computational cost threshold based classification with same features will also be shown.
ContributorsCheng, Long (Author) / Turaga, Pavan (Thesis advisor) / Arizona State University (Publisher)
Created2012
Description
Motion capture using cost-effective sensing technology is challenging and the huge success of Microsoft Kinect has been attracting researchers to uncover the potential of using this technology into computer vision applications. In this thesis, an upper-body motion analysis in a home-based system for stroke rehabilitation using novel RGB-D camera - Kinect is presented. We address this problem by first conducting a systematic analysis of the usability of Kinect for motion analysis in stroke rehabilitation. Then a hybrid upper body tracking approach is proposed which combines off-the-shelf skeleton tracking with a novel depth-fused mean shift tracking method. We proposed several kinematic features reliably extracted from the proposed inexpensive and portable motion capture system and classifiers that correlate torso movement to clinical measures of unimpaired and impaired. Experiment results show that the proposed sensing and analysis works reliably on measuring torso movement quality and is promising for end-point tracking. The system is currently being deployed for large-scale evaluations.
ContributorsDu, Tingfang (Author) / Turaga, Pavan (Thesis advisor) / Spanias, Andreas (Committee member) / Rikakis, Thanassis (Committee member) / Arizona State University (Publisher)
Created2012
Description
Image understanding has been playing an increasingly crucial role in vision applications. Sparse models form an important component in image understanding, since the statistics of natural images reveal the presence of sparse structure. Sparse methods lead to parsimonious models, in addition to being efficient for large scale learning. In sparse modeling, data is represented as a sparse linear combination of atoms from a "dictionary" matrix. This dissertation focuses on understanding different aspects of sparse learning, thereby enhancing the use of sparse methods by incorporating tools from machine learning. With the growing need to adapt models for large scale data, it is important to design dictionaries that can model the entire data space and not just the samples considered. By exploiting the relation of dictionary learning to 1-D subspace clustering, a multilevel dictionary learning algorithm is developed, and it is shown to outperform conventional sparse models in compressed recovery, and image denoising. Theoretical aspects of learning such as algorithmic stability and generalization are considered, and ensemble learning is incorporated for effective large scale learning. In addition to building strategies for efficiently implementing 1-D subspace clustering, a discriminative clustering approach is designed to estimate the unknown mixing process in blind source separation. By exploiting the non-linear relation between the image descriptors, and allowing the use of multiple features, sparse methods can be made more effective in recognition problems. The idea of multiple kernel sparse representations is developed, and algorithms for learning dictionaries in the feature space are presented. Using object recognition experiments on standard datasets it is shown that the proposed approaches outperform other sparse coding-based recognition frameworks. Furthermore, a segmentation technique based on multiple kernel sparse representations is developed, and successfully applied for automated brain tumor identification. Using sparse codes to define the relation between data samples can lead to a more robust graph embedding for unsupervised clustering. By performing discriminative embedding using sparse coding-based graphs, an algorithm for measuring the glomerular number in kidney MRI images is developed. Finally, approaches to build dictionaries for local sparse coding of image descriptors are presented, and applied to object recognition and image retrieval.
ContributorsJayaraman Thiagarajan, Jayaraman (Author) / Spanias, Andreas (Thesis advisor) / Frakes, David (Committee member) / Tepedelenlioğlu, Cihan (Committee member) / Turaga, Pavan (Committee member) / Arizona State University (Publisher)
Created2013
Description
This work is focused on modeling the reliability concerns in GaN HEMT technology. The two main reliability concerns in GaN HEMTs are electromechanical coupling and current collapse. A theoretical model was developed to model the piezoelectric polarization charge dependence on the applied gate voltage. As the sheet electron density in the channel increases, the influence of electromechanical coupling reduces as the electric field in the comprising layers reduces. A Monte Carlo device simulator that implements the theoretical model was developed to model the transport in GaN HEMTs. It is observed that with the coupled formulation, the drain current degradation in the device varies from 2%-18% depending on the gate voltage. Degradation reduces with the increase in the gate voltage due to the increase in the electron gas density in the channel. The output and transfer characteristics match very well with the experimental data. An electro-thermal device simulator was developed coupling the Monte Caro-Poisson solver with the energy balance solver for acoustic and optical phonons. An output current degradation of around 2-3 % at a drain voltage of 5V due to self-heating was observed. It was also observed that the electrostatics near the gate to drain region of the device changes due to the hot spot created in the device from self heating. This produces an electric field in the direction of accelerating the electrons from the channel to surface states. This will aid to the current collapse phenomenon in the device. Thus, the electric field in the gate to drain region is very critical for reliable performance of the device. Simulations emulating the charging of the surface states were also performed and matched well with experimental data. Methods to improve the reliability performance of the device were also investigated in this work. A shield electrode biased at source potential was used to reduce the electric field in the gate to drain extension region. The hot spot position was moved away from the critical gate to drain region towards the drain as the shield electrode length and dielectric thickness were being altered.
ContributorsPadmanabhan, Balaji (Author) / Vasileska, Dragica (Thesis advisor) / Goodnick, Stephen M (Committee member) / Alford, Terry L. (Committee member) / Venkatraman, Prasad (Committee member) / Arizona State University (Publisher)
Created2013