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Description
Over the last decade, deep neural networks also known as deep learning, combined with large databases and specialized hardware for computation, have made major strides in important areas such as computer vision, computational imaging and natural language processing. However, such frameworks currently suffer from some drawbacks. For example, it is generally not clear how the architectures are to be designed for different applications, or how the neural networks behave under different input perturbations and it is not easy to make the internal representations and parameters more interpretable. In this dissertation, I propose building constraints into feature maps, parameters and and design of algorithms involving neural networks for applications in low-level vision problems such as compressive imaging and multi-spectral image fusion, and high-level inference problems including activity and face recognition. Depending on the application, such constraints can be used to design architectures which are invariant/robust to certain nuisance factors, more efficient and, in some cases, more interpretable. Through extensive experiments on real-world datasets, I demonstrate these advantages of the proposed methods over conventional frameworks.
ContributorsLohit, Suhas Anand (Author) / Turaga, Pavan (Thesis advisor) / Spanias, Andreas (Committee member) / Li, Baoxin (Committee member) / Jayasuriya, Suren (Committee member) / Arizona State University (Publisher)
Created2019
Description
Disentangling latent spaces is an important research direction in the interpretability of unsupervised machine learning. Several recent works using deep learning are very effective at producing disentangled representations. However, in the unsupervised setting, there is no way to pre-specify which part of the latent space captures specific factors of variations. While this is generally a hard problem because of the non-existence of analytical expressions to capture these variations, there are certain factors like geometric
transforms that can be expressed analytically. Furthermore, in existing frameworks, the disentangled values are also not interpretable. The focus of this work is to disentangle these geometric factors of variations (which turn out to be nuisance factors for many applications) from the semantic content of the signal in an interpretable manner which in turn makes the features more discriminative. Experiments are designed to show the modularity of the approach with other disentangling strategies as well as on multiple one-dimensional (1D) and two-dimensional (2D) datasets, clearly indicating the efficacy of the proposed approach.
transforms that can be expressed analytically. Furthermore, in existing frameworks, the disentangled values are also not interpretable. The focus of this work is to disentangle these geometric factors of variations (which turn out to be nuisance factors for many applications) from the semantic content of the signal in an interpretable manner which in turn makes the features more discriminative. Experiments are designed to show the modularity of the approach with other disentangling strategies as well as on multiple one-dimensional (1D) and two-dimensional (2D) datasets, clearly indicating the efficacy of the proposed approach.
ContributorsKoneripalli Seetharam, Kaushik (Author) / Turaga, Pavan (Thesis advisor) / Papandreou-Suppappola, Antonia (Committee member) / Jayasuriya, Suren (Committee member) / Arizona State University (Publisher)
Created2019
Description
The advent of commercial inexpensive sensors and the advances in information and communication technology (ICT) have brought forth the era of pervasive Quantified-Self. Automatic diet monitoring is one of the most important aspects for Quantified-Self because it is vital for ensuring the well-being of patients suffering from chronic diseases as well as for providing a low cost means for maintaining the health for everyone else. Automatic dietary monitoring consists of: a) Determining the type and amount of food intake, and b) Monitoring eating behavior, i.e., time, frequency, and speed of eating. Although there are some existing techniques towards these ends, they suffer from issues of low accuracy and low adherence. To overcome these issues, multiple sensors were utilized because the availability of affordable sensors that can capture the different aspect information has the potential for increasing the available knowledge for Quantified-Self. For a), I envision an intelligent dietary monitoring system that automatically identifies food items by using the knowledge obtained from visible spectrum camera and infrared spectrum camera. This system is able to outperform the state-of-the-art systems for cooked food recognition by 25% while also minimizing user intervention. For b), I propose a novel methodology, IDEA that performs accurate eating action identification within eating episodes with an average F1-score of 0.92. This is an improvement of 0.11 for precision and 0.15 for recall for the worst-case users as compared to the state-of-the-art. IDEA uses only a single wrist-band which includes four sensors and provides feedback on eating speed every 2 minutes without obtaining any manual input from the user.
ContributorsLee, Junghyo (Author) / Gupta, Sandeep K.S. (Thesis advisor) / Banerjee, Ayan (Committee member) / Li, Baoxin (Committee member) / Chiou, Erin (Committee member) / Kudva, Yogish C. (Committee member) / Arizona State University (Publisher)
Created2019
Description
A critical problem for airborne, ship board, and land based radars operating in maritime or littoral environments is the detection, identification and tracking of targets against backscattering caused by the roughness of the sea surface. Statistical models, such as the compound K-distribution (CKD), were shown to accurately describe two separate structures of the sea clutter intensity fluctuations. The first structure is the texture that is associated with long sea waves and exhibits long temporal decorrelation period. The second structure is the speckle that accounts for reflections from multiple scatters and exhibits a short temporal decorrelation period from pulse to pulse. Existing methods for estimating the CKD model parameters do not include the thermal noise power, which is critical for real sea clutter processing. Estimation methods that include the noise power are either computationally intensive or require very large data records.
This work proposes two new approaches for accurately estimating all three CKD model parameters, including noise power. The first method integrates, in an iterative fashion, the noise power estimation, using one-dimensional nonlinear curve fitting,
with the estimation of the shape and scale parameters, using closed-form solutions in terms of the CKD intensity moments. The second method is similar to the first except it replaces integer-based intensity moments with fractional moments which have been shown to achieve more accurate estimates of the shape parameter. These new methods can be implemented in real time without requiring large data records. They can also achieve accurate estimation performance as demonstrated with simulated and real sea clutter observation datasets. The work also investigates the numerically computed Cram\'er-Rao lower bound (CRLB) of the variance of the shape parameter estimate using intensity observations in thermal noise with unknown power. Using the CRLB, the asymptotic estimation performance behavior of the new estimators is studied and compared to that of other estimators.
This work proposes two new approaches for accurately estimating all three CKD model parameters, including noise power. The first method integrates, in an iterative fashion, the noise power estimation, using one-dimensional nonlinear curve fitting,
with the estimation of the shape and scale parameters, using closed-form solutions in terms of the CKD intensity moments. The second method is similar to the first except it replaces integer-based intensity moments with fractional moments which have been shown to achieve more accurate estimates of the shape parameter. These new methods can be implemented in real time without requiring large data records. They can also achieve accurate estimation performance as demonstrated with simulated and real sea clutter observation datasets. The work also investigates the numerically computed Cram\'er-Rao lower bound (CRLB) of the variance of the shape parameter estimate using intensity observations in thermal noise with unknown power. Using the CRLB, the asymptotic estimation performance behavior of the new estimators is studied and compared to that of other estimators.
ContributorsNorthrop, Judith (Author) / Papandreou-Suppappola, Antonia (Thesis advisor) / Chakrabarti, Chaitali (Committee member) / Tepedelenlioğlu, Cihan (Committee member) / Maurer, Alexander (Committee member) / Arizona State University (Publisher)
Created2019
Description
The open nature of the wireless communication medium makes it inherently vulnerable to an active attack, wherein a malicious adversary (or jammer) transmits into the medium to disrupt the operation of the legitimate users. Therefore, developing techniques to manage the presence of a jammer and to characterize the effect of an attacker on the fundamental limits of wireless communication networks is important. This dissertation studies various Gaussian communication networks in the presence of such an adversarial jammer.
First of all, a standard Gaussian channel is considered in the presence of a jammer, known as a Gaussian arbitrarily-varying channel, but with list-decoding at the receiver. The receiver decodes a list of messages, instead of only one message, with the goal of the correct message being an element of the list. The capacity is characterized, and it is shown that under some transmitter's power constraints the adversary is able to suspend the communication between the legitimate users and make the capacity zero.
Next, generalized packing lemmas are introduced for Gaussian adversarial channels to achieve the capacity bounds for three Gaussian multi-user channels in the presence of adversarial jammers. Inner and outer bounds on the capacity regions of Gaussian multiple-access channels, Gaussian broadcast channels, and Gaussian interference channels are derived in the presence of malicious jammers. For the Gaussian multiple-access channels with jammer, the capacity bounds coincide. In this dissertation, the adversaries can send any arbitrary signals to the channel while none of the transmitter and the receiver knows the adversarial signals' distribution.
Finally, the capacity of the standard point-to-point Gaussian fading channel in the presence of one jammer is investigated under multiple scenarios of channel state information availability, which is the knowledge of exact fading coefficients. The channel state information is always partially or fully known at the receiver to decode the message while the transmitter or the adversary may or may not have access to this information. Here, the adversary model is the same as the previous cases with no knowledge about the user's transmitted signal except possibly the knowledge of the fading path.
First of all, a standard Gaussian channel is considered in the presence of a jammer, known as a Gaussian arbitrarily-varying channel, but with list-decoding at the receiver. The receiver decodes a list of messages, instead of only one message, with the goal of the correct message being an element of the list. The capacity is characterized, and it is shown that under some transmitter's power constraints the adversary is able to suspend the communication between the legitimate users and make the capacity zero.
Next, generalized packing lemmas are introduced for Gaussian adversarial channels to achieve the capacity bounds for three Gaussian multi-user channels in the presence of adversarial jammers. Inner and outer bounds on the capacity regions of Gaussian multiple-access channels, Gaussian broadcast channels, and Gaussian interference channels are derived in the presence of malicious jammers. For the Gaussian multiple-access channels with jammer, the capacity bounds coincide. In this dissertation, the adversaries can send any arbitrary signals to the channel while none of the transmitter and the receiver knows the adversarial signals' distribution.
Finally, the capacity of the standard point-to-point Gaussian fading channel in the presence of one jammer is investigated under multiple scenarios of channel state information availability, which is the knowledge of exact fading coefficients. The channel state information is always partially or fully known at the receiver to decode the message while the transmitter or the adversary may or may not have access to this information. Here, the adversary model is the same as the previous cases with no knowledge about the user's transmitted signal except possibly the knowledge of the fading path.
ContributorsHosseinigoki, Fatemeh (Author) / Kosut, Oliver (Thesis advisor) / Zhang, Junshan (Committee member) / Sankar, Lalitha (Committee member) / Bliss, Daniel (Committee member) / Arizona State University (Publisher)
Created2019
Description
The emergence of perovskite and practical efficiency limit to silicon solar cells has opened door for perovskite and silicon based tandems with the possibility to achieve >30% efficiency. However, there are material and optical challenges that have to be overcome for the success of these tandems. In this work the aim is to understand and improve the light management issues in silicon and perovskite based tandems through comprehensive optical modeling and simulation of current state of the art tandems and by characterizing the optical properties of new top and bottom cell materials. Moreover, to propose practical solutions to mitigate some of the optical losses.
Highest efficiency single-junction silicon and bottom silicon sub-cell in silicon based tandems employ monocrystalline silicon wafer textured with random pyramids. Therefore, the light trapping performance of random pyramids in silicon solar cells is established. An accurate three-dimensional height map of random pyramids is captured and ray-traced to record the angular distribution of light inside the wafer which shows random pyramids trap light as well as Lambertian scatterer.
Second, the problem of front-surface reflectance common to all modules, planar solar cells and to silicon and perovskite based tandems is dealt. A nano-imprint lithography procedure is developed to fabricate polydimethylsiloxane (PDMS) scattering layer carrying random pyramids that effectively reduces the reflectance. Results show it increased the efficiency of planar semi-transparent perovskite solar cell by 10.6% relative.
Next a detailed assessment of light-management in practical two-terminal perovskite/silicon and perovskite/perovskite tandems is performed to quantify reflectance, parasitic and light-trapping losses. For this first a methodology based on spectroscopic ellipsometry is developed to characterize new absorber materials employed in tandems. Characterized materials include wide-bandgap (CH3NH3I3, CsyFA1-yPb(BrxI1-x)3) and low-bandgap (Cs0.05FA0.5MA0.45(Pb0.5Sn0.5)I3) perovskites and wide-bandgap CdTe alloys (CdZnSeTe). Using this information rigorous optical modeling of two-terminal perovskite/silicon and perovskite/perovskite tandems with varying light management schemes is performed. Thus providing a guideline for further development.
Highest efficiency single-junction silicon and bottom silicon sub-cell in silicon based tandems employ monocrystalline silicon wafer textured with random pyramids. Therefore, the light trapping performance of random pyramids in silicon solar cells is established. An accurate three-dimensional height map of random pyramids is captured and ray-traced to record the angular distribution of light inside the wafer which shows random pyramids trap light as well as Lambertian scatterer.
Second, the problem of front-surface reflectance common to all modules, planar solar cells and to silicon and perovskite based tandems is dealt. A nano-imprint lithography procedure is developed to fabricate polydimethylsiloxane (PDMS) scattering layer carrying random pyramids that effectively reduces the reflectance. Results show it increased the efficiency of planar semi-transparent perovskite solar cell by 10.6% relative.
Next a detailed assessment of light-management in practical two-terminal perovskite/silicon and perovskite/perovskite tandems is performed to quantify reflectance, parasitic and light-trapping losses. For this first a methodology based on spectroscopic ellipsometry is developed to characterize new absorber materials employed in tandems. Characterized materials include wide-bandgap (CH3NH3I3, CsyFA1-yPb(BrxI1-x)3) and low-bandgap (Cs0.05FA0.5MA0.45(Pb0.5Sn0.5)I3) perovskites and wide-bandgap CdTe alloys (CdZnSeTe). Using this information rigorous optical modeling of two-terminal perovskite/silicon and perovskite/perovskite tandems with varying light management schemes is performed. Thus providing a guideline for further development.
ContributorsManzoor, Salman (Author) / Holman, Zachary C (Thesis advisor) / King, Richard (Committee member) / Goryll, Michael (Committee member) / Zhao, Yuji (Committee member) / Arizona State University (Publisher)
Created2019
Description
Climate change is affecting power generation globally. Increase in the ambient air
temperature due to the emission of greenhouse gases, caused mainly by burning of fossil fuels, is the most prominent reason for this effect. This increase in the temperature along with the changing precipitation levels has led to the melting of the snow packs and increase in the evaporation levels, thus affecting hydropower. The hydropower in the United States might increase by 8%-60% due to Representative Concentration Pathway (RCP) 4.5 and RCP 8.5 scenarios respectively by 2050. Wind power generation is mainly affected by the change in the wind speed and solar power generation is mainly affected by the increase in the ambient air temperature, changes in precipitation and solar radiation. Solar power output reduces by approximately a total of 2.5 billion kilowatt- hour (kWh) by 2050 for an increase in ambient air temperature of 1 degree Celsius. Increase in the ambient air and water temperature mainly affect the thermal power generation. An increase in the temperature as per the RCP 4.5 and RCP 8.5 climate change scenarios could decrease the total thermal power generation in the United States by an average of 26 billion kWh and a possible income loss of around 1.5 billion dollars. This thesis discusses the various effects of climate change on each of these four power plant types.
temperature due to the emission of greenhouse gases, caused mainly by burning of fossil fuels, is the most prominent reason for this effect. This increase in the temperature along with the changing precipitation levels has led to the melting of the snow packs and increase in the evaporation levels, thus affecting hydropower. The hydropower in the United States might increase by 8%-60% due to Representative Concentration Pathway (RCP) 4.5 and RCP 8.5 scenarios respectively by 2050. Wind power generation is mainly affected by the change in the wind speed and solar power generation is mainly affected by the increase in the ambient air temperature, changes in precipitation and solar radiation. Solar power output reduces by approximately a total of 2.5 billion kilowatt- hour (kWh) by 2050 for an increase in ambient air temperature of 1 degree Celsius. Increase in the ambient air and water temperature mainly affect the thermal power generation. An increase in the temperature as per the RCP 4.5 and RCP 8.5 climate change scenarios could decrease the total thermal power generation in the United States by an average of 26 billion kWh and a possible income loss of around 1.5 billion dollars. This thesis discusses the various effects of climate change on each of these four power plant types.
ContributorsPenmetsa, Vikramaditya (Author) / Holbert, Keith E. (Thesis advisor) / Hedman, Mojdeh (Committee member) / Wu, Meng (Committee member) / Arizona State University (Publisher)
Created2020
Description
Wurtzite (In, Ga, Al) N semiconductors, especially InGaN material systems, demonstrate immense promises for the high efficiency thin film photovoltaic (PV) applications for future generation. Their unique and intriguing merits include continuously tunable wide band gap from 0.70 eV to 3.4 eV, strong absorption coefficient on the order of ∼105 cm−1, superior radiation resistance under harsh environment, and high saturation velocities and high mobility. Calculation from the detailed balance model also revealed that in multi-junction (MJ) solar cell device, materials with band gaps higher than 2.4 eV are required to achieve PV efficiencies greater than 50%, which is practically and easily feasible for InGaN materials. Other state-of-art modeling on InGaN solar cells also demonstrate great potential for applications of III-nitride solar cells in four-junction solar cell devices as well as in the integration with a non-III-nitride junction in multi-junction devices.
This dissertation first theoretically analyzed loss mechanisms and studied the theoretical limit of PV performance of InGaN solar cells with a semi-analytical model. Then three device design strategies are proposed to study and improve PV performance: band polarization engineering, structural design and band engineering. Moreover, three physical mechanisms related to high temperature performance of InGaN solar cells have been thoroughly investigated: thermal reliability issue, enhanced external quantum efficiency (EQE) and conversion efficiency with rising temperatures and carrier dynamics and localization effects inside nonpolar m-plane InGaN quantum wells (QWs) at high temperatures. In the end several future work will also be proposed.
Although still in its infancy, past and projected future progress of device design will ultimately achieve this very goal that III-nitride based solar cells will be indispensable for today and future’s society, technologies and society.
This dissertation first theoretically analyzed loss mechanisms and studied the theoretical limit of PV performance of InGaN solar cells with a semi-analytical model. Then three device design strategies are proposed to study and improve PV performance: band polarization engineering, structural design and band engineering. Moreover, three physical mechanisms related to high temperature performance of InGaN solar cells have been thoroughly investigated: thermal reliability issue, enhanced external quantum efficiency (EQE) and conversion efficiency with rising temperatures and carrier dynamics and localization effects inside nonpolar m-plane InGaN quantum wells (QWs) at high temperatures. In the end several future work will also be proposed.
Although still in its infancy, past and projected future progress of device design will ultimately achieve this very goal that III-nitride based solar cells will be indispensable for today and future’s society, technologies and society.
ContributorsHuang, Xuanqi (Author) / Zhao, Yuji (Thesis advisor) / Goodnick, Stephen M. (Committee member) / King, Richard R. (Committee member) / Vasileska, Dragica (Committee member) / Arizona State University (Publisher)
Created2020
Description
A model of self-heating is incorporated into a Cellular Monte Carlo (CMC) particle-based device simulator through the solution of an energy balance equation (EBE) for phonons. The EBE self-consistently couples charge and heat transport in the simulation through a novel approach to computing the heat generation rate in the device under study. First, the moments of the Boltzmann Transport equation (BTE) are discussed, and subsequently the EBE of for phonons is derived. Subsequently, several tests are performed to verify the applicability and accuracy of a nonlinear iterative method for the solution of the EBE in the presence of convective boundary conditions, as compared to a finite element analysis solver as well as using the Kirchhoff transformation. The coupled electrothermal characterization of a GaN/AlGaN high electron mobility transistor (HEMT) is then performed, and the effects of non-ideal interfaces and boundary conditions are studied.
The proposed thermal model is then applied to a novel $\Pi$-gate architecture which has been suggested to reduce hot electron generation in the device, compared to the conventional T-gate. Additionally, small signal ac simulations are performed for the determination of cutoff frequencies using the thermal model as well.
Finally, further extensions of the CMC algorithm used in this work are discussed, including 1) higher-order moments of the phonon BTE, 2) coupling to phonon Monte Carlo simulations, and 3) application to other large-bandgap, and therefore high-power, materials such as diamond.
The proposed thermal model is then applied to a novel $\Pi$-gate architecture which has been suggested to reduce hot electron generation in the device, compared to the conventional T-gate. Additionally, small signal ac simulations are performed for the determination of cutoff frequencies using the thermal model as well.
Finally, further extensions of the CMC algorithm used in this work are discussed, including 1) higher-order moments of the phonon BTE, 2) coupling to phonon Monte Carlo simulations, and 3) application to other large-bandgap, and therefore high-power, materials such as diamond.
ContributorsMerrill, Ky (Author) / Saraniti, Marco (Thesis advisor) / Goodnick, Stephen (Committee member) / Smith, David (Committee member) / Wang, Robert (Committee member) / Arizona State University (Publisher)
Created2020
Description
An optimal energy scheduling procedure is essential in an isolated environment such as naval submarines. Conventional naval submarines include diesel-electric propulsion systems, which utilize diesel generators along with batteries and fuel cells. Submarines can charge the batteries by running diesel-electric generators only at the surface or at snorkeling depth. This is the most dangerous time for submarines to be detectable by acoustic and non-acoustic sensors of enemy assets. Optimizing the energy resources while reducing the need for snorkeling is the main factor to enhance underwater endurance. This thesis introduces an energy management system (EMS) as a supervisory tool for the officers onboard to plan energy schedules in order to complete various missions. The EMS for a 4,000-ton class conventional submarine is developed to minimize snorkeling and satisfy various conditions of practically designed missions by optimizing the energy resources, such as Lithium-ion batteries, Proton-exchange membrane fuel cells, and diesel-electric generators. Eventually, the optimized energy schedules with the minimum snorkeling hours are produced for five mission scenarios. More importantly, this EMS performs deterministic and stochastic operational scheduling processes to provide secured optimal schedules which contains outages in the power generation and storage systems.
ContributorsJeon, Byeongdoo (Author) / Hedman, Mojdeh Khorsand (Thesis advisor) / Holbert, Keith E. (Committee member) / Wu, Meng (Committee member) / Arizona State University (Publisher)
Created2020