Matching Items (19)
Filtering by
- All Subjects: Electrical Engineering
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
This dissertation proposes a miniature FIR filter that works at microwave frequencies, whose filter response can ideally be digitally programmed. Such a frequency agile device can find applications in cellular communications and wireless networking. The basic concept of the FIR filter utilizes a low loss acoustic waveguide of appropriate geometry that acts as a traveling wave tapped-delay line. The input RF signal is applied by an array of capacitive transducers at various locations on the acoustic waveguide at one end that excites waves of a propagating acoustic mode with varying spatial delays and amplitudes which interfere as they propagate. The output RF signal is picked up at the other end of the waveguide by another array of capacitive transducers. Tuning of the FIR filter coefficients is realized by controlling the DC voltage profile applied to the individual transducers which essentially shapes the overall filter response. Equivalent circuit modeling of the capacitive transducer, acoustic waveguide and transducer-line coupling is presented in this dissertation. A theoretical model for the filter is developed from a general theory of an array of transducers exciting a waveguide and is used to obtain a set of filter design equations. A MATLAB based circuit simulator is developed to simulate the filter responses. Design parameters and simulation results obtained for an example waveguide structure are presented and compared to the values estimated by the theoretical model. A waveguide structure utilizing the Rayleigh-like mode of a ridge is then introduced. A semi-analytical method to obtain propagating elastic modes of such a ridge waveguide etched in an anisotropic crystal is presented. Microfabrication of a filter based on ridges etched in single crystal Silicon is discussed along with details of the challenges faced. Finally, future work and a few alternative designs are presented that can have a better chance of success. Analysis and modeling work to this point has given a good understanding of the working principles, performance tradeoffs and fabrication pitfalls of the proposed device. With the appropriate acoustic waveguide structure, the proposed device could make it possible to realize miniature programmable FIR filters in the GHz range.
ContributorsGalinde, Ameya (Author) / Abbaspour-Tamijani, Abbas (Thesis advisor) / Chae, Junseok (Committee member) / Pan, George (Committee member) / Phillips, Stephen (Committee member) / Arizona State University (Publisher)
Created2013
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
Three dimensional (3-D) ultrasound is safe, inexpensive, and has been shown to drastically improve system ease-of-use, diagnostic efficiency, and patient throughput. However, its high computational complexity and resulting high power consumption has precluded its use in hand-held applications.
In this dissertation, algorithm-architecture co-design techniques that aim to make hand-held 3-D ultrasound a reality are presented. First, image enhancement methods to improve signal-to-noise ratio (SNR) are proposed. These include virtual source firing techniques and a low overhead digital front-end architecture using orthogonal chirps and orthogonal Golay codes.
Second, algorithm-architecture co-design techniques to reduce the power consumption of 3-D SAU imaging systems is presented. These include (i) a subaperture multiplexing strategy and the corresponding apodization method to alleviate the signal bandwidth bottleneck, and (ii) a highly efficient iterative delay calculation method to eliminate complex operations such as multiplications, divisions and square-root in delay calculation during beamforming. These techniques were used to define Sonic Millip3De, a 3-D die stacked architecture for digital beamforming in SAU systems. Sonic Millip3De produces 3-D high resolution images at 2 frames per second with system power consumption of 15W in 45nm technology.
Third, a new beamforming method based on separable delay decomposition is proposed to reduce the computational complexity of the beamforming unit in an SAU system. The method is based on minimizing the root-mean-square error (RMSE) due to delay decomposition. It reduces the beamforming complexity of a SAU system by 19x while providing high image fidelity that is comparable to non-separable beamforming. The resulting modified Sonic Millip3De architecture supports a frame rate of 32 volumes per second while maintaining power consumption of 15W in 45nm technology.
Next a 3-D plane-wave imaging system that utilizes both separable beamforming and coherent compounding is presented. The resulting system has computational complexity comparable to that of a non-separable non-compounding baseline system while significantly improving contrast-to-noise ratio and SNR. The modified Sonic Millip3De architecture is now capable of generating high resolution images at 1000 volumes per second with 9-fire-angle compounding.
In this dissertation, algorithm-architecture co-design techniques that aim to make hand-held 3-D ultrasound a reality are presented. First, image enhancement methods to improve signal-to-noise ratio (SNR) are proposed. These include virtual source firing techniques and a low overhead digital front-end architecture using orthogonal chirps and orthogonal Golay codes.
Second, algorithm-architecture co-design techniques to reduce the power consumption of 3-D SAU imaging systems is presented. These include (i) a subaperture multiplexing strategy and the corresponding apodization method to alleviate the signal bandwidth bottleneck, and (ii) a highly efficient iterative delay calculation method to eliminate complex operations such as multiplications, divisions and square-root in delay calculation during beamforming. These techniques were used to define Sonic Millip3De, a 3-D die stacked architecture for digital beamforming in SAU systems. Sonic Millip3De produces 3-D high resolution images at 2 frames per second with system power consumption of 15W in 45nm technology.
Third, a new beamforming method based on separable delay decomposition is proposed to reduce the computational complexity of the beamforming unit in an SAU system. The method is based on minimizing the root-mean-square error (RMSE) due to delay decomposition. It reduces the beamforming complexity of a SAU system by 19x while providing high image fidelity that is comparable to non-separable beamforming. The resulting modified Sonic Millip3De architecture supports a frame rate of 32 volumes per second while maintaining power consumption of 15W in 45nm technology.
Next a 3-D plane-wave imaging system that utilizes both separable beamforming and coherent compounding is presented. The resulting system has computational complexity comparable to that of a non-separable non-compounding baseline system while significantly improving contrast-to-noise ratio and SNR. The modified Sonic Millip3De architecture is now capable of generating high resolution images at 1000 volumes per second with 9-fire-angle compounding.
ContributorsYang, Ming (Author) / Chakrabarti, Chaitali (Thesis advisor) / Papandreou-Suppappola, Antonia (Committee member) / Karam, Lina (Committee member) / Frakes, David (Committee member) / Ogras, Umit Y. (Committee member) / Arizona State University (Publisher)
Created2015
Description
The ability to monitor electrophysiological signals from the sentient brain is requisite to decipher its enormously complex workings and initiate remedial solutions for the vast amount of neurologically-based disorders. Despite immense advancements in creating a variety of instruments to record signals from the brain, the translation of such neurorecording instrumentation to real clinical domains places heavy demands on their safety and reliability, both of which are not entirely portrayed by presently existing implantable recording solutions. In an attempt to lower these barriers, alternative wireless radar backscattering techniques are proposed to render the technical burdens of the implant chip to entirely passive neurorecording processes that transpire in the absence of formal integrated power sources or powering schemes along with any active circuitry. These radar-like wireless backscattering mechanisms are used to conceive of fully passive neurorecording operations of an implantable microsystem. The fully passive device potentially manifests inherent advantages over current wireless implantable and wired recording systems: negligible heat dissipation to reduce risks of brain tissue damage and minimal circuitry for long term reliability as a chronic implant. Fully passive neurorecording operations are realized via intrinsic nonlinear mixing properties of the varactor diode. These mixing and recording operations are directly activated by wirelessly interrogating the fully passive device with a microwave carrier signal. This fundamental carrier signal, acquired by the implant antenna, mixes through the varactor diode along with the internal targeted neuropotential brain signals to produce higher frequency harmonics containing the targeted neuropotential signals. These harmonics are backscattered wirelessly to the external interrogator that retrieves and recovers the original neuropotential brain signal. The passive approach removes the need for internal power sources and may alleviate heat trauma and reliability issues that limit practical implementation of existing implantable neurorecorders.
ContributorsSchwerdt, Helen N (Author) / Chae, Junseok (Thesis advisor) / Miranda, Félix A. (Committee member) / Phillips, Stephen (Committee member) / Towe, Bruce C (Committee member) / Balanis, Constantine A (Committee member) / Frakes, David (Committee member) / Arizona State University (Publisher)
Created2014
Description
Magnetic Resonance Imaging (MRI) is limited in speed and resolution by the inherently low Signal to Noise Ratio (SNR) of the underlying signal. Advances in sampling efficiency are required to support future improvements in scan time and resolution. SNR efficiency is improved by sampling data for a larger proportion of total imaging time. This is challenging as these acquisitions are typically subject to artifacts such as blurring and distortions. The current work proposes a set of tools to help with the creation of different types of SNR efficient scans. An SNR efficient pulse sequence providing diffusion imaging data with full brain coverage and minimal distortion is first introduced. The proposed method acquires single-shot, low resolution image slabs which are then combined to reconstruct the full volume. An iterative deblurring algorithm allowing the lengthening of spiral SPoiled GRadient echo (SPGR) acquisition windows in the presence of rapidly varying off-resonance fields is then presented. Finally, an efficient and practical way of collecting 3D reformatted data is proposed. This method constitutes a good tradeoff between 2D and 3D neuroimaging in terms of scan time and data presentation. These schemes increased the SNR efficiency of currently existing methods and constitute key enablers for the development of SNR efficient MRI.
ContributorsAboussouan, Eric (Author) / Frakes, David (Thesis advisor) / Pipe, James (Thesis advisor) / Debbins, Joseph (Committee member) / Towe, Bruce (Committee member) / Arizona State University (Publisher)
Created2011
Description
Power supply management is important for MEMS (Micro-Electro-Mechanical-Systems) bio-sensing and chemical sensing applications. The dissertation focuses on discussion of accessibility to different power sources and supply tuning in sensing applications. First, the dissertation presents a high efficiency DC-DC converter for a miniaturized Microbial Fuel Cell (MFC). The miniaturized MFC produces up to approximately 10µW with an output voltage of 0.4-0.7V. Such a low voltage, which is also load dependent, prevents the MFC to directly drive low power electronics. A PFM (Pulse Frequency Modulation) type DC-DC converter in DCM (Discontinuous Conduction Mode) is developed to address the challenges and provides a load independent output voltage with high conversion efficiency. The DC-DC converter, implemented in UMC 0.18µm technology, has been thoroughly characterized, coupled with the MFC. At 0.9V output, the converter has a peak efficiency of 85% with 9µW load, highest efficiency over prior publication. Energy could be harvested wirelessly and often has profound impacts on system performance. The dissertation reports a side-by-side comparison of two wireless and passive sensing systems: inductive and electromagnetic (EM) couplings for an application of in-situ and real-time monitoring of wafer cleanliness in semiconductor facilities. The wireless system, containing the MEMS sensor works with battery-free operations. Two wireless systems based on inductive and EM couplings have been implemented. The working distance of the inductive coupling system is limited by signal-to-noise-ratio (SNR) while that of the EM coupling is limited by the coupled power. The implemented on-wafer transponders achieve a working distance of 6 cm and 25 cm with a concentration resolution of less than 2% (4 ppb for a 200 ppb solution) for inductive and EM couplings, respectively. Finally, the supply tuning is presented in bio-sensing application to mitigate temperature sensitivity. The FBAR (film bulk acoustic resonator) based oscillator is an attractive method in label-free sensing application. Molecular interactions on FBAR surface induce mass change, which results in resonant frequency shift of FBAR. While FBAR has a high-Q to be sensitive to the molecular interactions, FBAR has finite temperature sensitivity. A temperature compensation technique is presented that improves the temperature coefficient of a 1.625 GHz FBAR-based oscillator from -118 ppm/K to less than 1 ppm/K by tuning the supply voltage of the oscillator. The tuning technique adds no additional component and has a large frequency tunability of -4305 ppm/V.
ContributorsZhang, Xu (Author) / Chae, Junseok (Thesis advisor) / Kiaei, Sayfe (Committee member) / Bakkaloglu, Bertan (Committee member) / Kozicki, Michael (Committee member) / Phillips, Stephen (Committee member) / Arizona State University (Publisher)
Created2012
Description
Recent advances in camera architectures and associated mathematical representations now enable compressive acquisition of images and videos at low data-rates. While most computer vision applications of today are composed of conventional cameras, which collect a large amount redundant data and power hungry embedded systems, which compress the collected data for further processing, compressive cameras offer the advantage of direct acquisition of data in compressed domain and hence readily promise to find applicability in computer vision, particularly in environments hampered by limited communication bandwidths. However, despite the significant progress in theory and methods of compressive sensing, little headway has been made in developing systems for such applications by exploiting the merits of compressive sensing. In such a setting, we consider the problem of activity recognition, which is an important inference problem in many security and surveillance applications. Since all successful activity recognition systems involve detection of human, followed by recognition, a potential fully functioning system motivated by compressive camera would involve the tracking of human, which requires the reconstruction of atleast the initial few frames to detect the human. Once the human is tracked, the recognition part of the system requires only the features to be extracted from the tracked sequences, which can be the reconstructed images or the compressed measurements of such sequences. However, it is desirable in resource constrained environments that these features be extracted from the compressive measurements without reconstruction. Motivated by this, in this thesis, we propose a framework for understanding activities as a non-linear dynamical system, and propose a robust, generalizable feature that can be extracted directly from the compressed measurements without reconstructing the original video frames. The proposed feature is termed recurrence texture and is motivated from recurrence analysis of non-linear dynamical systems. We show that it is possible to obtain discriminative features directly from the compressed stream and show its utility in recognition of activities at very low data rates.
ContributorsKulkarni, Kuldeep Sharad (Author) / Turaga, Pavan (Thesis advisor) / Spanias, Andreas (Committee member) / Frakes, David (Committee member) / Arizona State University (Publisher)
Created2012
Description
Human operators have difficulty driving cranes quickly, accurately, and safely because of the slow response of heavy crane structures, non-intuitive control interfaces, and payload oscillations. Recently, a novel hand-motion crane control system has been proposed to improve performance by coupling an intuitive control interface with an element that reduces the complex oscillatory behavior of the payload. Hand-motion control allows operators to drive a crane by simply moving a hand-held radio-frequency tag through the desired path. Real-time location sensors are used to track the movements of the tag and the tag position is used in a feedback control loop to drive the crane. An input shaper is added to eliminate dangerous payload oscillations. However, tag position measurements are corrupted by noise. It is important to understand the noise properties so that appropriate filters can be designed to mitigate the effects of noise and improve tracking accuracy. This work discusses implementing filtering techniques to address the issue of noise in the operating environment. Five different filters are used on experimentally-acquired tag trajectories to reduce noise. The filtered trajectories are then used to drive crane simulations. Filter performance is evaluated with respect to the energy usage of the crane trolley, the settling time of the crane payload oscillations, and the safety corridor of the crane trajectory. The effects of filter window lengths on these parameters are also investigated. An adaptive filtering technique, namely the Kalman filter, adapts to the noise characteristics of the workspace to minimize the tag tracking error and performs better than the other filtering techniques examined.
ContributorsRagunathan, Sudarshan (Author) / Frakes, David (Thesis advisor) / Singhose, William (Committee member) / Tillery, Stephen Helms (Committee member) / Arizona State University (Publisher)
Created2012
Description
There is a growing interest for improved high-accuracy camera calibration methods due to the increasing demand for 3D visual media in commercial markets. Camera calibration is used widely in the fields of computer vision, robotics and 3D reconstruction. Camera calibration is the first step for extracting 3D data from a 2D image. It plays a crucial role in computer vision and 3D reconstruction due to the fact that the accuracy of the reconstruction and 3D coordinate determination relies on the accuracy of the camera calibration to a great extent. This thesis presents a novel camera calibration method using a circular calibration pattern. The disadvantages and issues with existing state-of-the-art methods are discussed and are overcome in this work. The implemented system consists of techniques of local adaptive segmentation, ellipse fitting, projection and optimization. Simulation results are presented to illustrate the performance of the proposed scheme. These results show that the proposed method reduces the error as compared to the state-of-the-art for high-resolution images, and that the proposed scheme is more robust to blur in the imaged calibration pattern.
ContributorsPrakash, Charan Dudda (Author) / Karam, Lina J (Thesis advisor) / Frakes, David (Committee member) / Papandreou-Suppappola, Antonia (Committee member) / Arizona State University (Publisher)
Created2012