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School of Electrical and Computer Engineering

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Now showing 1 - 10 of 4460
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    An Audio-Band Discrete-Time ΔΣ ADC Using Ring-amp and KT/C Noise Cancellation in 45nm CMOS
    (Georgia Institute of Technology, 2023-12-18) Yao, Huang
    In this thesis, we designed an audio-band DT ΔΣ ADC using ring amplifiers and kT/C noise cancellation. The ADC is designed and simulated in NCSU 45nm CMOS with a 1.2V supply. It achieves a peak SNDR and DR of 100dB and 105dB with a 2mW power consumption, yielding a Schreier Figure of Merit (FoM) of 171.dB and a Walden FoM of 482fJ/step. We show that a ring amplifier is a good candidate for low-voltage DT circuits such as an SC integrator, and the kT/C noise cancellation technique can provide a better noise performance in a ΔΣ ADC. In the future, we would optimize the amplifier to improve the noise performance further and to have lower power consumption
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    Online Decision Making Under Information-Theoretic Constraints
    (Georgia Institute of Technology, 2023-12-15) Chang, Meng-Che
    We consider the problems of online decision-making under two categories of information theoretical constraints, namely, security and communication constraints. Three types of security constraints considered in this thesis are secrecy, covertness, and robustness, where the objectives are to make the adversary have little knowledge about the unknown parameter, to make the decision-making process undetectable, and to make the decision-making algorithm robust to adversarial attacks, respectively. We formulate the decision-making problems with these three types of security-related constraints formally and analyze the performance of decision-making algorithms under these constraints. In the second half of this thesis, we analyze the tradeoff between the detection error exponent and the communication rate under the framework of joint communication and sensing. Both mono-static and bi-static joint communication and sensing models are considered in the thesis. Finally, an extension to the case when the transmission window varies with observations is also made to emphasize the benefit of adaptivity.
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    Data driven approaches to address inaccurate nosology in mental health from neuroimaging data
    (Georgia Institute of Technology, 2023-12-14) Rokham, Hooman
    This research addresses the pervasive challenge of label noise in machine learning, particularly in sensitive domains like medical applications and psychiatry. Label noise, stemming from sources such as inadequate information and human error, poses a significant threat to the accuracy of classification models, especially in medical imaging where mislabeled data can lead to harmful outcomes. In the realm of psychiatry, existing categorizations of psychosis face complexities due to unreliability and heterogeneity. The research aims to achieve two key objectives: first, to develop robust frameworks and algorithms for detecting and rectifying incorrect labels in datasets, focusing on semi-supervised auto-labeling approaches to enhance data homogeneity. Second, the research delves into biomarker discovery for mood and psychosis disorders, seeking to unveil latent patterns within neuroimaging data that could serve as vital biomarkers, revolutionizing diagnostic and classification methods for these complex mental health conditions.
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    Explicit Group Sparse Projection for Machine Learning
    (Georgia Institute of Technology, 2023-12-14) Ohib, Riyasat
    The concept of sparse solutions in classical machine learning is noted for its efficiency and has parallels in the natural world, such as in the mammalian visual cortex. This biological basis provides an inspiration for the importance of sparsity in computational models. Sparsity is increasingly relevant in machine learning, especially in non-negative matrix factorization (NMF), where it aids in interpretability and efficiency. NMF involves breaking down a non-negative matrix into simpler components, with sparsity ensuring these components distinctly represent data features, simplifying interpretation. In deep learning, sparse model parameters lead to more efficient computation, quicker training and inference, and in some cases, more robust models. As models grow in size, the role of inducing sparsity becomes even more crucial. In this thesis, we design a new sparse projection method for a set of vectors that guarantees a desired average sparsity level measured leveraging the popular Hoyer measure. Existing approaches either project each vector individually or require the use of a regularization parameter which implicitly maps to the average $\ell_0$-measure of sparsity. Instead, in our approach we set the \revise{Hoyer} sparsity level for the whole set explicitly and simultaneously project a group of vectors with the \revise{Hoyer} sparsity level of each vector tuned automatically. Hence, we call this the Group Sparse Projection (GSP). We show that the computational complexity of our projection operator is linear in the size of the problem. GSP can be used in particular to sparsify the columns of a matrix, which we use to compute sparse low-rank matrix approximations (namely, sparse NMF). We showcase the efficacy of our approach in both supervised and unsupervised learning tasks on image datasets including MNIST and CIFAR10. In non-negative matrix factorization, our approach yields competitive reconstruction errors against state-of-the-art algorithms. In neural network pruning, the sparse models produced by our method have competitive accuracy at corresponding sparsity values compared to existing methods.
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    Path-Based Differential Algorithm and Graph Theory-Based Analysis on Neuroimaging Data
    (Georgia Institute of Technology, 2023-12-14) Falakshahi, Haleh
    Graph theoretical methods have emerged as crucial tools for exploring the intricate networks within the human brain, spanning disciplines such as neuroscience, cognitive science, psychiatry, psychology, and the study of brain disorders and development. However, research in this realm has traditionally concentrated on assessing local and global graph metrics, inadvertently neglecting the rich information embedded within the intricate paths that interconnect distinct brain regions. This gap in knowledge motivated the development of an innovative algorithm aimed at identifying multi-step paths in patient groups by comparing them to control cohorts. Following path identification, a covariance decomposition approach is employed to delve into the connections shared between pairs of brain nodes, offering a deeper understanding of network dynamics. The application of this methodology is exemplified through the analysis of resting-state functional MRI data from individuals with schizophrenia, yielding valuable insights into the presence of disconnectors within and between specific functional domains, with a particular focus on the default mode and cognitive control networks. Additionally, an extensive longitudinal study investigates the processes associated with healthy aging, employing advanced neuroimaging techniques and cognitive assessments. This comprehensive approach spans from individuals in their mid-30s to centenarians, revealing dynamic changes in brain networks. These findings underscore the importance of considering both static and dynamic network characteristics and highlight specific graph metrics that hold relevance in elucidating the cognitive changes associated with the aging process. Furthermore, the proposed path analysis algorithm detects disrupted pathways, shedding light on potential path-based biomarkers. Altogether, these research endeavors expand our understanding of brain network dynamics in health and disease, with implications for both clinical applications and the broader study of brain function.
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    Overcoming Longstanding Synthesis Challenges Toward Realizing the Full Device Potential of III-Nitride Semiconductors
    (Georgia Institute of Technology, 2023-12-13) Matthews, Chris
    III-nitrides have the potential to address a large number of fundamental needs for semiconductor device applications, including full-spectrum/tandem-with-silicon solar cells (InGaN), RGB LEDs (InGaN), UV LEDs and lasers (AlGaN), and high-power diodes and transistors (AlGaN). However, many of these applications remain unrealized due to challenges in growing high-quality material by traditional growth techniques like metalorganic chemical vapor deposition (MOCVD) and molecular beam epitaxy (MBE). Through control of growth kinetics, metal-modulated epitaxy (MME) has been shown to have success in growing III-nitrides, especially highly doped films and ternary alloys with compositions in the miscibility gaps unreachable by other techniques. This control of growth kinetics is expected to lead to the realization of devices that have driven interest in this material system but have thus far been unachievable. This dissertation focuses on progressing the understanding of material synthesis and properties at the extreme ends of the III-nitride material range, with a particular emphasis on InGaN, AlGaN, and AlN. The history of InGaN synthesis as it relates to phase separation is reviewed, and a revised definition of phase separation is proposed. The prior assumption of spinodal decomposition in III-nitrides is re-examined and found to be unlikely due to the density and packing of these materials. Phase separation is reconsidered as a function of surface processes, especially for epitaxy by physical vapor deposition methods such as MBE and MME. A set of surface processes (thermal decomposition, lateral cation separation, vertical cation segregation, and preferential incorporation) are proposed to contribute to phase separation in InGaN, AlInN, and even AlGaN. This revised definition of phase separation in III-nitrides is discussed as needing further examination experimentally, theoretically, or both. The range of growth conditions for MME is much larger than for MOCVD or MBE, and the throughput of all of these techniques is low, so it is necessary to develop a model that can quantitatively describe the growth kinetics under any growth condition in order to simplify the task of quantifying the revised definition of phase separation and eventually realizing devices of interest. Such a model is described, implemented, and evaluated against experimental data from phase-separated AlGaN. This model is simplified to only include vertical cation segregation and preferential incorporation due to the high thermal stability of AlGaN and high growth rates used in MME. Both mechanisms are found to be important in modeling phase separation in AlGaN that is similar to the experimental data. Previously, a major impediment to AlN semiconductor device progress was achieving high, bulk carrier concentrations through impurity doping. Low-temperature epitaxial methods are investigated and found to play a key role in enabling the doping of AlN and the eventual realization of AlN-based semiconductor devices. Both silicon and beryllium doping of AlN are hindered by temperature-dependent processes during epitaxy, such as lattice expansion, dopant desorption, and generation of compensating impurities. Using metal-modulated epitaxy to grow AlN at low temperatures, p- and n-type AlN films with carrier concentrations of 4.4 × 1018 cm-3 and 6 × 1018 cm-3 and resistivities of 0.045 Ω-cm and 0.02 Ω-cm, respectively, are achieved. Doping and defect states in doped aluminum nitride films are examined via cathodoluminescence (CL) spectroscopy. Energy levels within the band gap are observed and potential associated defects are proposed. Fermi-Dirac statistics are used to identify three effective donor states in Si-doped AlN and a single effective acceptor energy in Be-doped AlN. CL investigation reveals near-band-edge and defect luminescence for both n- and p-type AlN films. AlN is found to be a promising optoelectronic material, but requires significant further study on contaminant and defect mitigation before high-quality devices can be realized.
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    Design of a Long-Range and Retrodirective Tunneling Tag
    (Georgia Institute of Technology, 2023-12-13) Saetia, Christopher Austin
    The rise of the Internet of Things has increasingly integrated radio-frequency identification (RFID) technology in many practical applications that deal with localization, communication, and more. Specifically, RFID tags are designed to be low-powered and inexpensively deployable. Passive tags in particular harvest energy from ambient transmitted signals in their environment to operate their circuitry and backscatter a response on the same transmitted signal. They do not depend on an internal battery or power source; hence, their communication range with a reader is usually limited compared to a tag or sensor node that has its own power source and dedicated transmitter. To meet the vision of deploying backscatter, passive tags on a large scale, new tag architecture for improved read-range needs to be investigated. This thesis aims to design a 915 MHz retrodirective, tunneling tag by loading a rat-race coupler with tunneling reflection amplifiers. The thesis’ goal is to explore whether this proposed design has improved backscatter capabilities to help improve its read-range and investigate the effects of retrodirectivity and reflection amplification on the tag’s overall response.
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    Hardware Security at the Edge: A Discussion of Hardware Security Challenges and Tradeoffs Under Resource Constraints with Selected Example Primitives
    (Georgia Institute of Technology, 2023-12-13) Ellis, Zachary
    The explosion of smart devices into all realms of day-to-day life has been of great benefit to humanity allowing the measurement and control of many parts of the everyday in an automated fashion. Consumers can now choose to monitor parts of their home or collect data on their body to stay more informed on their health and spending. This unprecedented era of personalized data also poses a great security challenge for individuals and corporations to keep this potentially sensitive data secure. Many of these new devices operate in mobile or otherwise low power settings and are at a constraint for resources maximizing accuracy and communication speed. This work aims to address the impact of added security measures to these devices on a hardware level, quantifying the area, power, and speed impacts of added security with two example hardware primitives that may be used in edge devices now and in the future.
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    Anti-Jam Measures For Protected Tactical Waveform
    (Georgia Institute of Technology, 2023-12-13) Moturu, Amar
    This thesis explores various possible improvements to the Protected Tactical Waveform standard. These improvements mainly center on the log-on process in which users initially attempt to access the network and how certain signal processing techniques can be employed in order to combat jamming effects. Improvements are explored in the domain of the initial synchronization process as well as in the areas of adaptive antenna arrays, direction of arrival estimation, and beamforming techniques. The performance of these improvements are characterized both in DVB-S2 and PTW within a MATLAB simulation framework.
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    Robotic System to Motivate Long Term Infant Kicking for Motor Development Progression
    (Georgia Institute of Technology, 2023-12-12) Emeli, Victor
    The spontaneous kicking patterns of an infant provide markers that may predict the progression of motor development. Consistent atypical kicking behaviors can forecast irregularities in future development. One main indicator of impaired motor development is the progressive advancement of spasticity in muscle groups. For at-risk infants, physical therapy that encourages kicking motions can help reduce the onset of spasticity, especially if initiated at an early age. Traditionally, physical therapy is conducted by health professionals in a clinical setting, which can be labor intensive and costly. A method that increases physical therapy opportunities by providing an in-home system that motivates kicking motions and operates without immediate clinical supervision would be beneficial. We introduce a system that utilizes 3D computer vision and a robotic infant mobile to detect spontaneous kicking patterns and activate mobile stimuli to encourage prolonged kicking activity. The visual classification of kick or non-kick activity is used to activate the mobile stimuli with the goal of encouraging continued kicking patterns. We also employ statistical techniques to calculate kick amplitude, kick intensity, and kick deviation. These parameters provide insight into kick features and provide measurements to validate the influence of the mobile stimuli on kicking behavior. Additionally, we develop algorithms to identify mobile stimuli preferences that are unique to each infant for encouraging prolonged kicking activity. Finally, we investigate methods for reducing the complexity of the system by employing 2D data estimation for real-world use cases.