Matching Items (110)
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- All Subjects: Electrical Engineering
- Creators: Yu, Hongbin
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
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
Fluxgate sensors are magnetic field sensors that can measure DC and low frequency AC magnetic fields. They can measure much lower magnetic fields than other magnetic sensors like Hall effect sensors, magnetoresistive sensors etc. They also have high linearity, high sensitivity and low noise. The major application of fluxgate sensors is in magnetometers for the measurement of earth's magnetic field. Magnetometers are used in navigation systems and electronic compasses. Fluxgate sensors can also be used to measure high DC currents. Integrated micro-fluxgate sensors have been developed in recent years. These sensors have much lower power consumption and area compared to their PCB counterparts. The output voltage of micro-fluxgate sensors is very low which makes the analog front end more complex and results in an increase in power consumption of the system. In this thesis a new analog front-end circuit for micro-fluxgate sensors is developed. This analog front-end circuit uses charge pump based excitation circuit and phase delay based read-out chain. With these two features the power consumption of analog front-end is reduced. The output is digital and it is immune to amplitude noise at the output of the sensor. Digital output is produced without using an ADC. A SPICE model of micro-fluxgate sensor is used to verify the operation of the analog front-end and the simulation results show very good linearity.
ContributorsPappu, Karthik (Author) / Bakkaloglu, Bertan (Thesis advisor) / Christen, Jennifer Blain (Committee member) / Yu, Hongbin (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
Effective modeling of high dimensional data is crucial in information processing and machine learning. Classical subspace methods have been very effective in such applications. However, over the past few decades, there has been considerable research towards the development of new modeling paradigms that go beyond subspace methods. This dissertation focuses on the study of sparse models and their interplay with modern machine learning techniques such as manifold, ensemble and graph-based methods, along with their applications in image analysis and recovery. By considering graph relations between data samples while learning sparse models, graph-embedded codes can be obtained for use in unsupervised, supervised and semi-supervised problems. Using experiments on standard datasets, it is demonstrated that the codes obtained from the proposed methods outperform several baseline algorithms. In order to facilitate sparse learning with large scale data, the paradigm of ensemble sparse coding is proposed, and different strategies for constructing weak base models are developed. Experiments with image recovery and clustering demonstrate that these ensemble models perform better when compared to conventional sparse coding frameworks. When examples from the data manifold are available, manifold constraints can be incorporated with sparse models and two approaches are proposed to combine sparse coding with manifold projection. The improved performance of the proposed techniques in comparison to sparse coding approaches is demonstrated using several image recovery experiments. In addition to these approaches, it might be required in some applications to combine multiple sparse models with different regularizations. In particular, combining an unconstrained sparse model with non-negative sparse coding is important in image analysis, and it poses several algorithmic and theoretical challenges. A convex and an efficient greedy algorithm for recovering combined representations are proposed. Theoretical guarantees on sparsity thresholds for exact recovery using these algorithms are derived and recovery performance is also demonstrated using simulations on synthetic data. Finally, the problem of non-linear compressive sensing, where the measurement process is carried out in feature space obtained using non-linear transformations, is considered. An optimized non-linear measurement system is proposed, and improvements in recovery performance are demonstrated in comparison to using random measurements as well as optimized linear measurements.
ContributorsNatesan Ramamurthy, Karthikeyan (Author) / Spanias, Andreas (Thesis advisor) / Tsakalis, Konstantinos (Committee member) / Karam, Lina (Committee member) / Turaga, Pavan (Committee member) / Arizona State University (Publisher)
Created2013
Description
This thesis summarizes modeling and simulation of plasmonic waveguides and nanolasers. The research includes modeling of dielectric constants of doped semiconductor as a potential plasmonic material, simulation of plasmonic waveguides with different configurations and geometries, simulation and design of plasmonic nanolasers. In the doped semiconductor part, a more accurate model accounting for dielectric constant of doped InAs was proposed. In the model, Interband transitions accounted for by Adachi's model considering Burstein-Moss effect and free electron effect governed by Drude model dominate in different spectral regions. For plasmonic waveguide part, Insulator-Metal-Insulator (IMI) waveguide, silver nanowire waveguide with and without substrate, Metal-Semiconductor-Metal (MSM) waveguide and Metal-Insulator-Semiconductor-Insulator-Metal (MISIM) waveguide were investigated respectively. Modal analysis was given for each part. Lastly, a comparative study of plasmonic and optical modes in an MSM disk cavity was performed by FDTD simulation for room temperature at the telecommunication wavelength. The results show quantitatively that plasmonic modes have advantages over optical modes in the scalability down to small size and the cavity Quantum Electrodynamics(QED) effects due to the possibility of breaking the diffraction limit. Surprisingly for lasing characteristics, though plasmonic modes have large loss as expected, minimal achievable threshold can be attained for whispering gallery plasmonic modes with azimuthal number of 2 by optimizing cavity design at 1.55µm due to interplay of metal loss and radiation loss.
ContributorsWang, Haotong (Author) / Ning, Cunzheng (Thesis advisor) / Palais, Joseph (Committee member) / Yu, Hongbin (Committee member) / Arizona State University (Publisher)
Created2014
Description
Semiconductor nanowires are important candidates for highly scaled three dimensional electronic devices. It is very advantageous to combine their scaling capability with the high yield of planar CMOS technology by integrating nanowire devices into planar circuits. The purpose of this research is to identify the challenges associated with the fabrication of vertically oriented Si and Ge nanowire diodes and modeling their electrical behavior so that they can be utilized to create unique three dimensional architectures that can boost the scaling of electronic devices into the next generation. In this study, vertical Ge and Si nanowire Schottky diodes have been fabricated using bottom-up vapor-liquid-solid (VLS) and top-down reactive ion etching (RIE) approaches respectively. VLS growth yields nanowires with atomically smooth sidewalls at sub-50 nm diameters but suffers from the problem that the doping increases radially outwards from the core of the devices. RIE is much faster than VLS and does not suffer from the problem of non-uniform doping. However, it yields nanowires with rougher sidewalls and gets exceedingly inefficient in yielding vertical nanowires for diameters below 50 nm. The I-V characteristics of both Ge and Si nanowire diodes cannot be adequately fit by the thermionic emission model. Annealing in forming gas which passivates dangling bonds on the nanowire surface is shown to have a considerable impact on the current through the Si nanowire diodes indicating that fixed charges and traps on the surface of the devices play a major role in determining their electrical behavior. Also, due to the vertical geometry of the nanowire diodes, electric field lines originating from the metal and terminating on their sidewalls can directly modulate their conductivity. Both these effects have to be included in the model aimed at predicting the current through vertical nanowire diodes. This study shows that the current through vertical nanowire diodes cannot be predicted accurately using the thermionic emission model which is suitable for planar devices and identifies the factors needed to build a comprehensive analytical model for predicting the current through vertically oriented nanowire diodes.
ContributorsChandra, Nishant (Author) / Goodnick, Stephen M (Thesis advisor) / Tracy, Clarence J. (Committee member) / Yu, Hongbin (Committee member) / Ferry, David K. (Committee member) / Arizona State University (Publisher)
Created2014