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- Genre: Academic theses
- Creators: Chakrabarti, Chaitali
Description
Vehicles powered by electricity and alternative-fuels are becoming a more popular form of transportation since they have less of an environmental impact than standard gasoline vehicles. Unfortunately, their success is currently inhibited by the sparseness of locations where the vehicles can refuel as well as the fact that many of the vehicles have a range that is less than those powered by gasoline. These factors together create a "range anxiety" in drivers, which causes the drivers to worry about the utility of alternative-fuel and electric vehicles and makes them less likely to purchase these vehicles. For the new vehicle technologies to thrive it is critical that range anxiety is minimized and performance is increased as much as possible through proper routing and scheduling. In the case of long distance trips taken by individual vehicles, the routes must be chosen such that the vehicles take the shortest routes while not running out of fuel on the trip. When many vehicles are to be routed during the day, if the refueling stations have limited capacity then care must be taken to avoid having too many vehicles arrive at the stations at any time. If the vehicles that will need to be routed in the future are unknown then this problem is stochastic. For fleets of vehicles serving scheduled operations, switching to alternative-fuels requires ensuring the schedules do not cause the vehicles to run out of fuel. This is especially problematic since the locations where the vehicles may refuel are limited due to the technology being new. This dissertation covers three related optimization problems: routing a single electric or alternative-fuel vehicle on a long distance trip, routing many electric vehicles in a network where the stations have limited capacity and the arrivals into the system are stochastic, and scheduling fleets of electric or alternative-fuel vehicles with limited locations to refuel. Different algorithms are proposed to solve each of the three problems, of which some are exact and some are heuristic. The algorithms are tested on both random data and data relating to the State of Arizona.
ContributorsAdler, Jonathan D (Author) / Mirchandani, Pitu B. (Thesis advisor) / Askin, Ronald (Committee member) / Gel, Esma (Committee member) / Xue, Guoliang (Committee member) / Zhang, Muhong (Committee member) / Arizona State University (Publisher)
Created2014
Description
Major advancements in biology and medicine have been realized during recent decades, including massively parallel sequencing, which allows researchers to collect millions or billions of short reads from a DNA or RNA sample. This capability opens the door to a renaissance in personalized medicine if effectively deployed. Three projects that address major and necessary advancements in massively parallel sequencing are included in this dissertation. The first study involves a pair of algorithms to verify patient identity based on single nucleotide polymorphisms (SNPs). In brief, we developed a method that allows de novo construction of sample relationships, e.g., which ones are from the same individuals and which are from different individuals. We also developed a method to confirm the hypothesis that a tumor came from a known individual. The second study derives an algorithm to multiplex multiple Polymerase Chain Reaction (PCR) reactions, while minimizing interference between reactions that compromise results. PCR is a powerful technique that amplifies pre-determined regions of DNA and is often used to selectively amplify DNA and RNA targets that are destined for sequencing. It is highly desirable to multiplex reactions to save on reagent and assay setup costs as well as equalize the effect of minor handling issues across gene targets. Our solution involves a binary integer program that minimizes events that are likely to cause interference between PCR reactions. The third study involves design and analysis methods required to analyze gene expression and copy number results against a reference range in a clinical setting for guiding patient treatments. Our goal is to determine which events are present in a given tumor specimen. These events may be mutation, DNA copy number or RNA expression. All three techniques are being used in major research and diagnostic projects for their intended purpose at the time of writing this manuscript. The SNP matching solution has been selected by The Cancer Genome Atlas to determine sample identity. Paradigm Diagnostics, Viomics and International Genomics Consortium utilize the PCR multiplexing technique to multiplex various types of PCR reactions on multi-million dollar projects. The reference range-based normalization method is used by Paradigm Diagnostics to analyze results from every patient.
ContributorsMorris, Scott (Author) / Gel, Esma S (Thesis advisor) / Runger, George C. (Thesis advisor) / Askin, Ronald (Committee member) / Paulauskis, Joseph (Committee member) / Arizona State University (Publisher)
Created2014
Description
Structural integrity is an important characteristic of performance for critical components used in applications such as aeronautics, materials, construction and transportation. When appraising the structural integrity of these components, evaluation methods must be accurate. In addition to possessing capability to perform damage detection, the ability to monitor the level of damage over time can provide extremely useful information in assessing the operational worthiness of a structure and in determining whether the structure should be repaired or removed from service. In this work, a sequential Bayesian approach with active sensing is employed for monitoring crack growth within fatigue-loaded materials. The monitoring approach is based on predicting crack damage state dynamics and modeling crack length observations. Since fatigue loading of a structural component can change while in service, an interacting multiple model technique is employed to estimate probabilities of different loading modes and incorporate this information in the crack length estimation problem. For the observation model, features are obtained from regions of high signal energy in the time-frequency plane and modeled for each crack length damage condition. Although this observation model approach exhibits high classification accuracy, the resolution characteristics can change depending upon the extent of the damage. Therefore, several different transmission waveforms and receiver sensors are considered to create multiple modes for making observations of crack damage. Resolution characteristics of the different observation modes are assessed using a predicted mean squared error criterion and observations are obtained using the predicted, optimal observation modes based on these characteristics. Calculation of the predicted mean square error metric can be computationally intensive, especially if performed in real time, and an approximation method is proposed. With this approach, the real time computational burden is decreased significantly and the number of possible observation modes can be increased. Using sensor measurements from real experiments, the overall sequential Bayesian estimation approach, with the adaptive capability of varying the state dynamics and observation modes, is demonstrated for tracking crack damage.
ContributorsHuff, Daniel W (Author) / Papandreou-Suppappola, Antonia (Thesis advisor) / Kovvali, Narayan (Committee member) / Chakrabarti, Chaitali (Committee member) / Chattopadhyay, Aditi (Committee member) / Arizona State University (Publisher)
Created2013
Description
Texture analysis plays an important role in applications like automated pattern inspection, image and video compression, content-based image retrieval, remote-sensing, medical imaging and document processing, to name a few. Texture Structure Analysis is the process of studying the structure present in the textures. This structure can be expressed in terms of perceived regularity. Our human visual system (HVS) uses the perceived regularity as one of the important pre-attentive cues in low-level image understanding. Similar to the HVS, image processing and computer vision systems can make fast and efficient decisions if they can quantify this regularity automatically. In this work, the problem of quantifying the degree of perceived regularity when looking at an arbitrary texture is introduced and addressed. One key contribution of this work is in proposing an objective no-reference perceptual texture regularity metric based on visual saliency. Other key contributions include an adaptive texture synthesis method based on texture regularity, and a low-complexity reduced-reference visual quality metric for assessing the quality of synthesized textures. In order to use the best performing visual attention model on textures, the performance of the most popular visual attention models to predict the visual saliency on textures is evaluated. Since there is no publicly available database with ground-truth saliency maps on images with exclusive texture content, a new eye-tracking database is systematically built. Using the Visual Saliency Map (VSM) generated by the best visual attention model, the proposed texture regularity metric is computed. The proposed metric is based on the observation that VSM characteristics differ between textures of differing regularity. The proposed texture regularity metric is based on two texture regularity scores, namely a textural similarity score and a spatial distribution score. In order to evaluate the performance of the proposed regularity metric, a texture regularity database called RegTEX, is built as a part of this work. It is shown through subjective testing that the proposed metric has a strong correlation with the Mean Opinion Score (MOS) for the perceived regularity of textures. The proposed method is also shown to be robust to geometric and photometric transformations and outperforms some of the popular texture regularity metrics in predicting the perceived regularity. The impact of the proposed metric to improve the performance of many image-processing applications is also presented. The influence of the perceived texture regularity on the perceptual quality of synthesized textures is demonstrated through building a synthesized textures database named SynTEX. It is shown through subjective testing that textures with different degrees of perceived regularities exhibit different degrees of vulnerability to artifacts resulting from different texture synthesis approaches. This work also proposes an algorithm for adaptively selecting the appropriate texture synthesis method based on the perceived regularity of the original texture. A reduced-reference texture quality metric for texture synthesis is also proposed as part of this work. The metric is based on the change in perceived regularity and the change in perceived granularity between the original and the synthesized textures. The perceived granularity is quantified through a new granularity metric that is proposed in this work. It is shown through subjective testing that the proposed quality metric, using just 2 parameters, has a strong correlation with the MOS for the fidelity of synthesized textures and outperforms the state-of-the-art full-reference quality metrics on 3 different texture databases. Finally, the ability of the proposed regularity metric in predicting the perceived degradation of textures due to compression and blur artifacts is also established.
ContributorsVaradarajan, Srenivas (Author) / Karam, Lina J (Thesis advisor) / Chakrabarti, Chaitali (Committee member) / Li, Baoxin (Committee member) / Tepedelenlioğlu, Cihan (Committee member) / Arizona State University (Publisher)
Created2014
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
Audio signals, such as speech and ambient sounds convey rich information pertaining to a user’s activity, mood or intent. Enabling machines to understand this contextual information is necessary to bridge the gap in human-machine interaction. This is challenging due to its subjective nature, hence, requiring sophisticated techniques. This dissertation presents a set of computational methods, that generalize well across different conditions, for speech-based applications involving emotion recognition and keyword detection, and ambient sounds-based applications such as lifelogging.
The expression and perception of emotions varies across speakers and cultures, thus, determining features and classification methods that generalize well to different conditions is strongly desired. A latent topic models-based method is proposed to learn supra-segmental features from low-level acoustic descriptors. The derived features outperform state-of-the-art approaches over multiple databases. Cross-corpus studies are conducted to determine the ability of these features to generalize well across different databases. The proposed method is also applied to derive features from facial expressions; a multi-modal fusion overcomes the deficiencies of a speech only approach and further improves the recognition performance.
Besides affecting the acoustic properties of speech, emotions have a strong influence over speech articulation kinematics. A learning approach, which constrains a classifier trained over acoustic descriptors, to also model articulatory data is proposed here. This method requires articulatory information only during the training stage, thus overcoming the challenges inherent to large-scale data collection, while simultaneously exploiting the correlations between articulation kinematics and acoustic descriptors to improve the accuracy of emotion recognition systems.
Identifying context from ambient sounds in a lifelogging scenario requires feature extraction, segmentation and annotation techniques capable of efficiently handling long duration audio recordings; a complete framework for such applications is presented. The performance is evaluated on real world data and accompanied by a prototypical Android-based user interface.
The proposed methods are also assessed in terms of computation and implementation complexity. Software and field programmable gate array based implementations are considered for emotion recognition, while virtual platforms are used to model the complexities of lifelogging. The derived metrics are used to determine the feasibility of these methods for applications requiring real-time capabilities and low power consumption.
The expression and perception of emotions varies across speakers and cultures, thus, determining features and classification methods that generalize well to different conditions is strongly desired. A latent topic models-based method is proposed to learn supra-segmental features from low-level acoustic descriptors. The derived features outperform state-of-the-art approaches over multiple databases. Cross-corpus studies are conducted to determine the ability of these features to generalize well across different databases. The proposed method is also applied to derive features from facial expressions; a multi-modal fusion overcomes the deficiencies of a speech only approach and further improves the recognition performance.
Besides affecting the acoustic properties of speech, emotions have a strong influence over speech articulation kinematics. A learning approach, which constrains a classifier trained over acoustic descriptors, to also model articulatory data is proposed here. This method requires articulatory information only during the training stage, thus overcoming the challenges inherent to large-scale data collection, while simultaneously exploiting the correlations between articulation kinematics and acoustic descriptors to improve the accuracy of emotion recognition systems.
Identifying context from ambient sounds in a lifelogging scenario requires feature extraction, segmentation and annotation techniques capable of efficiently handling long duration audio recordings; a complete framework for such applications is presented. The performance is evaluated on real world data and accompanied by a prototypical Android-based user interface.
The proposed methods are also assessed in terms of computation and implementation complexity. Software and field programmable gate array based implementations are considered for emotion recognition, while virtual platforms are used to model the complexities of lifelogging. The derived metrics are used to determine the feasibility of these methods for applications requiring real-time capabilities and low power consumption.
ContributorsShah, Mohit (Author) / Spanias, Andreas (Thesis advisor) / Chakrabarti, Chaitali (Thesis advisor) / Berisha, Visar (Committee member) / Turaga, Pavan (Committee member) / Arizona State University (Publisher)
Created2015
Description
Tracking a time-varying number of targets is a challenging
dynamic state estimation problem whose complexity is intensified
under low signal-to-noise ratio (SNR) or high clutter conditions.
This is important, for example, when tracking
multiple, closely spaced targets moving in the same direction such as a
convoy of low observable vehicles moving through a forest or multiple
targets moving in a crisscross pattern. The SNR in
these applications is usually low as the reflected signals from
the targets are weak or the noise level is very high.
An effective approach for detecting and tracking a single target
under low SNR conditions is the track-before-detect filter (TBDF)
that uses unthresholded measurements. However, the TBDF has only been used to
track a small fixed number of targets at low SNR.
This work proposes a new multiple target TBDF approach to track a
dynamically varying number of targets under the recursive Bayesian framework.
For a given maximum number of
targets, the state estimates are obtained by estimating the joint
multiple target posterior probability density function under all possible
target
existence combinations. The estimation of the corresponding target existence
combination probabilities and the target existence probabilities are also
derived. A feasible sequential Monte Carlo (SMC) based implementation
algorithm is proposed. The approximation accuracy of the SMC
method with a reduced number of particles is improved by an efficient
proposal density function that partitions the multiple target space into a
single target space.
The proposed multiple target TBDF method is extended to track targets in sea
clutter using highly time-varying radar measurements. A generalized
likelihood function for closely spaced multiple targets in compound Gaussian
sea clutter is derived together with the maximum likelihood estimate of
the model parameters using an iterative fixed point algorithm.
The TBDF performance is improved by proposing a computationally feasible
method to estimate the space-time covariance matrix of rapidly-varying sea
clutter. The method applies the Kronecker product approximation to the
covariance matrix and uses particle filtering to solve the resulting dynamic
state space model formulation.
dynamic state estimation problem whose complexity is intensified
under low signal-to-noise ratio (SNR) or high clutter conditions.
This is important, for example, when tracking
multiple, closely spaced targets moving in the same direction such as a
convoy of low observable vehicles moving through a forest or multiple
targets moving in a crisscross pattern. The SNR in
these applications is usually low as the reflected signals from
the targets are weak or the noise level is very high.
An effective approach for detecting and tracking a single target
under low SNR conditions is the track-before-detect filter (TBDF)
that uses unthresholded measurements. However, the TBDF has only been used to
track a small fixed number of targets at low SNR.
This work proposes a new multiple target TBDF approach to track a
dynamically varying number of targets under the recursive Bayesian framework.
For a given maximum number of
targets, the state estimates are obtained by estimating the joint
multiple target posterior probability density function under all possible
target
existence combinations. The estimation of the corresponding target existence
combination probabilities and the target existence probabilities are also
derived. A feasible sequential Monte Carlo (SMC) based implementation
algorithm is proposed. The approximation accuracy of the SMC
method with a reduced number of particles is improved by an efficient
proposal density function that partitions the multiple target space into a
single target space.
The proposed multiple target TBDF method is extended to track targets in sea
clutter using highly time-varying radar measurements. A generalized
likelihood function for closely spaced multiple targets in compound Gaussian
sea clutter is derived together with the maximum likelihood estimate of
the model parameters using an iterative fixed point algorithm.
The TBDF performance is improved by proposing a computationally feasible
method to estimate the space-time covariance matrix of rapidly-varying sea
clutter. The method applies the Kronecker product approximation to the
covariance matrix and uses particle filtering to solve the resulting dynamic
state space model formulation.
ContributorsEbenezer, Samuel P (Author) / Papandreou-Suppappola, Antonia (Thesis advisor) / Chakrabarti, Chaitali (Committee member) / Bliss, Daniel (Committee member) / Kovvali, Narayan (Committee member) / Arizona State University (Publisher)
Created2015
Description
The aging process due to Bias Temperature Instability (both NBTI and PBTI) and Channel Hot Carrier (CHC) is a key limiting factor of circuit lifetime in CMOS design. Threshold voltage shift due to BTI is a strong function of stress voltage and temperature complicating stress and recovery prediction. This poses a unique challenge for long-term aging prediction for wide range of stress patterns. Traditional approaches usually resort to an average stress waveform to simplify the lifetime prediction. They are efficient, but fail to capture circuit operation, especially under dynamic voltage scaling (DVS) or in analog/mixed signal designs where the stress waveform is much more random. This work presents a suite of modelling solutions for BTI that enable aging simulation under all possible stress conditions. Key features of this work are compact models to predict BTI aging based on Reaction-Diffusion theory when the stress voltage is varying. The results to both reaction-diffusion (RD) and trapping-detrapping (TD) mechanisms are presented to cover underlying physics. Silicon validation of these models is performed at 28nm, 45nm and 65nm technology nodes, at both device and circuit levels. Efficient simulation leveraging the BTI models under DVS and random input waveform is applied to both digital and analog representative circuits such as ring oscillators and LNA. Both physical mechanisms are combined into a unified model which improves prediction accuracy at 45nm and 65nm nodes. Critical failure condition is also illustrated based on NBTI and PBTI at 28nm. A comprehensive picture for duty cycle shift is shown. DC stress under clock gating schemes results in monotonic shift in duty cycle which an AC stress causes duty cycle to converge close to 50% value. Proposed work provides a general and comprehensive solution to aging analysis under random stress patterns under BTI.
Channel hot carrier (CHC) is another dominant degradation mechanism which affects analog and mixed signal circuits (AMS) as transistor operates continuously in saturation condition. New model is proposed to account for e-e scattering in advanced technology nodes due to high gate electric field. The model is validated with 28nm and 65nm thick oxide data for different stress voltages. It demonstrates shift in worst case CHC condition to Vgs=Vds from Vgs=0.5Vds. A novel iteration based aging simulation framework for AMS designs is proposed which eliminates limitation for conventional reliability tools. This approach helps us identify a unique positive feedback mechanism termed as Bias Runaway. Bias runaway, is rapid increase of the bias voltage in AMS circuits which occurs when the feedback between the bias current and the effect of channel hot carrier turns into positive. The degradation of CHC is a gradual process but under specific circumstances, the degradation rate can be dramatically accelerated. Such a catastrophic phenomenon is highly sensitive to the initial operation condition, as well as transistor gate length. Based on 65nm silicon data, our work investigates the critical condition that triggers bias runaway, and the impact of gate length tuning. We develop new compact models as well as the simulation methodology for circuit diagnosis, and propose design solutions and the trade-offs to avoid bias runaway, which is vitally important to reliable AMS designs.
Channel hot carrier (CHC) is another dominant degradation mechanism which affects analog and mixed signal circuits (AMS) as transistor operates continuously in saturation condition. New model is proposed to account for e-e scattering in advanced technology nodes due to high gate electric field. The model is validated with 28nm and 65nm thick oxide data for different stress voltages. It demonstrates shift in worst case CHC condition to Vgs=Vds from Vgs=0.5Vds. A novel iteration based aging simulation framework for AMS designs is proposed which eliminates limitation for conventional reliability tools. This approach helps us identify a unique positive feedback mechanism termed as Bias Runaway. Bias runaway, is rapid increase of the bias voltage in AMS circuits which occurs when the feedback between the bias current and the effect of channel hot carrier turns into positive. The degradation of CHC is a gradual process but under specific circumstances, the degradation rate can be dramatically accelerated. Such a catastrophic phenomenon is highly sensitive to the initial operation condition, as well as transistor gate length. Based on 65nm silicon data, our work investigates the critical condition that triggers bias runaway, and the impact of gate length tuning. We develop new compact models as well as the simulation methodology for circuit diagnosis, and propose design solutions and the trade-offs to avoid bias runaway, which is vitally important to reliable AMS designs.
ContributorsSutaria, Ketul (Author) / Cao, Yu (Thesis advisor) / Bakkaloglu, Bertan (Committee member) / Chakrabarti, Chaitali (Committee member) / Yu, Shimeng (Committee member) / Arizona State University (Publisher)
Created2014
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
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