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- Genre: Masters Thesis
- Creators: Chakrabarti, Chaitali
- Member of: ASU Electronic Theses and Dissertations
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
ABSTRACT Developing new non-traditional device models is gaining popularity as the silicon-based electrical device approaches its limitation when it scales down. Membrane systems, also called P systems, are a new class of biological computation model inspired by the way cells process chemical signals. Spiking Neural P systems (SNP systems), a certain kind of membrane systems, is inspired by the way the neurons in brain interact using electrical spikes. Compared to the traditional Boolean logic, SNP systems not only perform similar functions but also provide a more promising solution for reliable computation. Two basic neuron types, Low Pass (LP) neurons and High Pass (HP) neurons, are introduced. These two basic types of neurons are capable to build an arbitrary SNP neuron. This leads to the conclusion that these two basic neuron types are Turing complete since SNP systems has been proved Turing complete. These two basic types of neurons are further used as the elements to construct general-purpose arithmetic circuits, such as adder, subtractor and comparator. In this thesis, erroneous behaviors of neurons are discussed. Transmission error (spike loss) is proved to be equivalent to threshold error, which makes threshold error discussion more universal. To improve the reliability, a new structure called motif is proposed. Compared to Triple Modular Redundancy improvement, motif design presents its efficiency and effectiveness in both single neuron and arithmetic circuit analysis. DRAM-based CMOS circuits are used to implement the two basic types of neurons. Functionality of basic type neurons is proved using the SPICE simulations. The motif improved adder and the comparator, as compared to conventional Boolean logic design, are much more reliable with lower leakage, and smaller silicon area. This leads to the conclusion that SNP system could provide a more promising solution for reliable computation than the conventional Boolean logic.
ContributorsAn, Pei (Author) / Cao, Yu (Thesis advisor) / Barnaby, Hugh (Committee member) / Chakrabarti, Chaitali (Committee member) / Arizona State University (Publisher)
Created2013
Description
With increasing transistor volume and reducing feature size, it has become a major design constraint to reduce power consumption also. This has given rise to aggressive architectural changes for on-chip power management and rapid development to energy efficient hardware accelerators. Accordingly, the objective of this research work is to facilitate software developers to leverage these hardware techniques and improve energy efficiency of the system. To achieve this, I propose two solutions for Linux kernel: Optimal use of these architectural enhancements to achieve greater energy efficiency requires accurate modeling of processor power consumption. Though there are many models available in literature to model processor power consumption, there is a lack of such models to capture power consumption at the task-level. Task-level energy models are a requirement for an operating system (OS) to perform real-time power management as OS time multiplexes tasks to enable sharing of hardware resources. I propose a detailed design methodology for constructing an architecture agnostic task-level power model and incorporating it into a modern operating system to build an online task-level power profiler. The profiler is implemented inside the latest Linux kernel and validated for Intel Sandy Bridge processor. It has a negligible overhead of less than 1\% hardware resource consumption. The profiler power prediction was demonstrated for various application benchmarks from SPEC to PARSEC with less than 4\% error. I also demonstrate the importance of the proposed profiler for emerging architectural techniques through use case scenarios, which include heterogeneous computing and fine grained per-core DVFS. Along with architectural enhancement in general purpose processors to improve energy efficiency, hardware accelerators like Coarse Grain reconfigurable architecture (CGRA) are gaining popularity. Unlike vector processors, which rely on data parallelism, CGRA can provide greater flexibility and compiler level control making it more suitable for present SoC environment. To provide streamline development environment for CGRA, I propose a flexible framework in Linux to do design space exploration for CGRA. With accurate and flexible hardware models, fine grained integration with accurate architectural simulator, and Linux memory management and DMA support, a user can carry out limitless experiments on CGRA in full system environment.
ContributorsDesai, Digant Pareshkumar (Author) / Vrudhula, Sarma (Thesis advisor) / Chakrabarti, Chaitali (Committee member) / Wu, Carole-Jean (Committee member) / Arizona State University (Publisher)
Created2013
Description
Micro Electro Mechanical Systems (MEMS) is one of the fastest growing field in silicon industry. Low cost production is key for any company to improve their market share. MEMS testing is challenging since input to test a MEMS device require physical stimulus like acceleration, pressure etc. Also, MEMS device vary with process and requires calibration to make them reliable. This increases test cost and testing time. This challenge can be overcome by combining electrical stimulus based testing along with statistical analysis on MEMS response for electrical stimulus and also limited physical stimulus response data. This thesis proposes electrical stimulus based built in self test(BIST) which can be used to get MEMS data and later this data can be used for statistical analysis. A capacitive MEMS accelerometer is considered to test this BIST approach. This BIST circuit overhead is less and utilizes most of the standard readout circuit. This thesis discusses accelerometer response for electrical stimulus and BIST architecture. As a part of this BIST circuit, a second order sigma delta modulator has been designed. This modulator has a sampling frequency of 1MHz and bandwidth of 6KHz. SNDR of 60dB is achieved with 1Vpp differential input signal and 3.3V supply
ContributorsKundur, Vinay (Author) / Bakkaloglu, Bertan (Committee member) / Ozev, Sule (Committee member) / Kiaei, Sayfe (Committee member) / Arizona State University (Publisher)
Created2013
Description
The applications which use MEMS accelerometer have been on rise and many new fields which are using the MEMS devices have been on rise. The industry is trying to reduce the cost of production of these MEMS devices. These devices are manufactured using micromachining and the interface circuitry is manufactured using CMOS and the final product is integrated on to a single chip. Amount spent on testing of the MEMS devices make up a considerable share of the total final cost of the device. In order to save the cost and time spent on testing, researchers have been trying to develop different methodologies. At present, MEMS devices are tested using mechanical stimuli to measure the device parameters and for calibration the device. This testing is necessary since the MEMS process is not a very well controlled process unlike CMOS. This is done using an ATE and the cost of using ATE (automatic testing equipment) contribute to 30-40% of the devices final cost. This thesis proposes an architecture which can use an Electrical Signal to stimulate the MEMS device and use the data from the MEMS response in approximating the calibration coefficients efficiently. As a proof of concept, we have designed a BIST (Built-in self-test) circuit for MEMS accelerometer. The BIST has an electrical stimulus generator, Capacitance-to-voltage converter, ∑ ∆ ADC. This thesis explains in detail the design of the Electrical stimulus generator. We have also designed a technique to correlate the parameters obtained from electrical stimuli to those obtained by mechanical stimuli. This method is cost effective since the additional circuitry needed to implement BIST is less since the technique utilizes most of the existing standard readout circuitry already present.
ContributorsJangala Naga, Naveen Sai (Author) / Ozev, Sule (Thesis advisor) / Bakkaloglu, Bertan (Committee member) / Kiaei, Sayfe (Committee member) / Arizona State University (Publisher)
Created2014
Description
Stream computing has emerged as an importantmodel of computation for embedded system applications particularly in the multimedia and network processing domains. In recent past several programming languages and embedded multi-core processors have been proposed for streaming applications. This thesis examines the execution and dynamic scheduling of stream programs on embedded multi-core processors. The thesis addresses the problem in the context of a multi-tasking environment with a time varying allocation of processing elements for a particular streaming application. As a solution the thesis proposes a two step approach where the stream program is compiled to gather key application information, and to generate re-targetable code. A light weight dynamic scheduler incorporates the second stage of the approach. The dynamic scheduler utilizes the static information and available resources to assign or partition the application across the multi-core architecture. The objective of the dynamic scheduler is to maximize the throughput of the application, and it is sensitive to the resource (processing elements, scratch-pad memory, DMA bandwidth) constraints imposed by the target architecture. We evaluate the proposed approach by compiling and scheduling benchmark stream programs on a representative embedded multi-core processor. We present experimental results that evaluate the quality of the solutions generated by the proposed approach by comparisons with existing techniques.
ContributorsLee, Haeseung (Author) / Chatha, Karamvir (Thesis advisor) / Vrudhula, Sarma (Committee member) / Chakrabarti, Chaitali (Committee member) / Wu, Carole-Jean (Committee member) / Arizona State University (Publisher)
Created2013
Description
Neural activity tracking using electroencephalography (EEG) and magnetoencephalography (MEG) brain scanning methods has been widely used in the field of neuroscience to provide insight into the nervous system. However, the tracking accuracy depends on the presence of artifacts in the EEG/MEG recordings. Artifacts include any signals that do not originate from neural activity, including physiological artifacts such as eye movement and non-physiological activity caused by the environment.
This work proposes an integrated method for simultaneously tracking multiple neural sources using the probability hypothesis density particle filter (PPHDF) and reducing the effect of artifacts using feature extraction and stochastic modeling. Unique time-frequency features are first extracted using matching pursuit decomposition for both neural activity and artifact signals.
The features are used to model probability density functions for each signal type using Gaussian mixture modeling for use in the PPHDF neural tracking algorithm. The probability density function of the artifacts provides information to the tracking algorithm that can help reduce the probability of incorrectly estimating the dynamically varying number of current dipole sources and their corresponding neural activity localization parameters. Simulation results demonstrate the effectiveness of the proposed algorithm in increasing the tracking accuracy performance for multiple dipole sources using recordings that have been contaminated by artifacts.
This work proposes an integrated method for simultaneously tracking multiple neural sources using the probability hypothesis density particle filter (PPHDF) and reducing the effect of artifacts using feature extraction and stochastic modeling. Unique time-frequency features are first extracted using matching pursuit decomposition for both neural activity and artifact signals.
The features are used to model probability density functions for each signal type using Gaussian mixture modeling for use in the PPHDF neural tracking algorithm. The probability density function of the artifacts provides information to the tracking algorithm that can help reduce the probability of incorrectly estimating the dynamically varying number of current dipole sources and their corresponding neural activity localization parameters. Simulation results demonstrate the effectiveness of the proposed algorithm in increasing the tracking accuracy performance for multiple dipole sources using recordings that have been contaminated by artifacts.
ContributorsJiang, Jiewei (Author) / Papandreou-Suppappola, Antonia (Thesis advisor) / Bliss, Daniel (Committee member) / Chakrabarti, Chaitali (Committee member) / Arizona State University (Publisher)
Created2014
Description
The research objective is fully differential op-amp with common mode feedback, which are applied in filter, band gap, Analog Digital Converter (ADC) and so on as a fundamental component in analog circuit. Having modeled various defect and analyzed corresponding probability, defect library could be built after reduced defect simulation.Based on the resolution of microscope scan tool, all these defects are categorized into four groups of defects by both function and location, bias circuit defect, first stage amplifier defect, output stage defect and common mode feedback defect, separately. Each fault result is attributed to one of these four region defects.Therefore, analog testing algorithm and automotive tool could be generated to assist testing engineers to meet the demand of large numbers of chips.
ContributorsLu, Zhijian (Author) / Ozev, Sule (Thesis advisor) / Kiaei, Sayfe (Committee member) / Ogras, Umit Y. (Committee member) / Arizona State University (Publisher)
Created2014
Description
Mobile platforms are becoming highly heterogeneous by combining a powerful multiprocessor system-on-chip (MpSoC) with numerous resources including display, memory, power management IC (PMIC), battery and wireless modems into a compact package. Furthermore, the MpSoC itself is a heterogeneous resource that integrates many processing elements such as CPU cores, GPU, video, image, and audio processors. As a result, optimization approaches targeting mobile computing needs to consider the platform at various levels of granularity.
Platform energy consumption and responsiveness are two major considerations for mobile systems since they determine the battery life and user satisfaction, respectively. In this work, the models for power consumption, response time, and energy consumption of heterogeneous mobile platforms are presented. Then, these models are used to optimize the energy consumption of baseline platforms under power, response time, and temperature constraints with and without introducing new resources. It is shown, the optimal design choices depend on dynamic power management algorithm, and adding new resources is more energy efficient than scaling existing resources alone. The framework is verified through actual experiments on Qualcomm Snapdragon 800 based tablet MDP/T. Furthermore, usage of the framework at both design and runtime optimization is also presented.
Platform energy consumption and responsiveness are two major considerations for mobile systems since they determine the battery life and user satisfaction, respectively. In this work, the models for power consumption, response time, and energy consumption of heterogeneous mobile platforms are presented. Then, these models are used to optimize the energy consumption of baseline platforms under power, response time, and temperature constraints with and without introducing new resources. It is shown, the optimal design choices depend on dynamic power management algorithm, and adding new resources is more energy efficient than scaling existing resources alone. The framework is verified through actual experiments on Qualcomm Snapdragon 800 based tablet MDP/T. Furthermore, usage of the framework at both design and runtime optimization is also presented.
ContributorsGupta, Ujjwala (Author) / Ogras, Umit Y. (Thesis advisor) / Ozev, Sule (Committee member) / Chakrabarti, Chaitali (Committee member) / Arizona State University (Publisher)
Created2014
Description
High speed current-steering DACs with high linearity are needed in today's applications such as wired and wireless communications, instrumentation, radar, and other direct digital synthesis (DDS) applications. However, a trade-off exists between the speed and resolution of Nyquist rate current-steering DACs. As the resolution increases, more transistor area is required to meet matching requirements for optimal linearity and thus, the overall speed of the DAC is limited.
In this thesis work, a 12-bit current-steering DAC was designed with current sources scaled below the required matching size to decrease the area and increase the overall speed of the DAC. By scaling the current sources, however, errors due to random mismatch between current sources will arise and additional calibration hardware is necessary to ensure 12-bit linearity. This work presents how to implement a self-calibration DAC that works to fix amplitude errors while maintaining a lower overall area. Additionally, the DAC designed in this thesis investigates the implementation feasibility of a data-interleaved architecture. Data interleaving can increase the total bandwidth of the DACs by 2 with an increase in SQNR by an additional 3 dB.
The final results show that the calibration method can effectively improve the linearity of the DAC. The DAC is able to run up to 400 MSPS frequencies with a 75 dB SFDR performance and above 87 dB SFDR performance at update rates of 200 MSPS.
In this thesis work, a 12-bit current-steering DAC was designed with current sources scaled below the required matching size to decrease the area and increase the overall speed of the DAC. By scaling the current sources, however, errors due to random mismatch between current sources will arise and additional calibration hardware is necessary to ensure 12-bit linearity. This work presents how to implement a self-calibration DAC that works to fix amplitude errors while maintaining a lower overall area. Additionally, the DAC designed in this thesis investigates the implementation feasibility of a data-interleaved architecture. Data interleaving can increase the total bandwidth of the DACs by 2 with an increase in SQNR by an additional 3 dB.
The final results show that the calibration method can effectively improve the linearity of the DAC. The DAC is able to run up to 400 MSPS frequencies with a 75 dB SFDR performance and above 87 dB SFDR performance at update rates of 200 MSPS.
ContributorsJankunas, Benjamin (Author) / Bakkaloglu, Bertan (Thesis advisor) / Kitchen, Jennifer (Committee member) / Ozev, Sule (Committee member) / Arizona State University (Publisher)
Created2014