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Description
Alzheimer's disease (AD) and Alzheimer's Related Dementias (ADRD) is projected to affect 50 million people globally in the coming decades. Clinical research suggests that Mild Cognitive Impairment (MCI), a precursor to dementia, offers a critical window for lifestyle interventions to delay or prevent the progression of AD/ADRD. Previous research indicates that lifestyle changes, including increased physical exercise, reduced caloric intake, and mentally stimulating exercises, can reduce the risk of MCI. Early detection of MCI is challenging due to subtle and often unnoticed cognitive decline, traditionally monitored through infrequent clinical tests. As part of this research, the Smart Driving System was proposed, a novel, unobtrusive, and economical technology to detect early stages of neurodegenerative diseases. This system, leveraging a multi-modal biosensing array (MMS) and AI algorithms, assesses daily driving behavior, offering insights into a driver's cognitive function. The ultimate goal is to develop the Smart Driving Device and App, integrating it into vehicles, and validating its effectiveness in detecting MCI through comprehensive pilot studies. The Smart Driving System represents a breakthrough in AD/ADRD management, promising significant improvements in early detection and offering a scalable, cost-effective solution for monitoring cognitive health in real-world settings.
ContributorsSerhan, Peter (Author) / Forzani, Erica (Thesis advisor) / Wu, Teresa (Committee member) / Hihath, Joshua (Committee member) / Arizona State University (Publisher)
Created2024
Description
With the significant advancements of wireless communication systems that aim to meet exponentially increasing data rate demands, two promising concepts have appeared: (i) Cell-free massive MIMO, which entails the joint transmission and processing of the signals allowing the removal of classical cell boundaries, and (ii) integrated sensing and communication (ISAC), unifying communication and sensing in a single framework. This dissertation aims to take steps toward overcoming the key challenges in each concept and eventually merge them for efficient future communication and sensing networks.Cell-free massive MIMO is a distributed MIMO concept that eliminates classical cell boundaries and provides a robust performance. A significant challenge in realizing the cell-free massive MIMO in practice is its deployment complexity. In particular, connecting its many distributed access points with the central processing unit through wired fronthaul is an expensive and time-consuming approach. To eliminate this problem and enhance scalability, in this dissertation, a cell-free massive MIMO architecture adopting a wireless fronthaul is proposed, and the optimization of achievable rates for the end-to-end system is carried out. The evaluation has shown the strong potential of employing wireless fronthaul in cell-free massive MIMO systems. ISAC merges radar and communication systems, allowing effective sharing of resources, including bandwidth and hardware. The ISAC framework also enables sensing to aid communications, which shows a significant potential in mobile communication applications. Specifically, radar sensing data can address challenges like beamforming overhead and blockages associated with higher frequency, large antenna arrays, and narrow beams. To that end, this dissertation develops radar-aided beamforming and blockage prediction approaches using low-cost radar devices and evaluates them in real-world systems to verify their potential. At the intersection of these two paradigms, the integration of sensing into cell-free massive MIMO systems emerges as an intriguing prospect for future technologies. This integration, however, presents the challenge of considering both sensing and communication objectives within a distributed system. With the motivation of overcoming this challenge, this dissertation investigates diverse beamforming and power allocation solutions. Comprehensive evaluations have shown that the incorporation of sensing objectives into joint beamforming designs offers substantial capabilities for next-generation wireless communication and sensing systems.
ContributorsDemirhan, Umut (Author) / Alkhateeb, Ahmed (Thesis advisor) / Dasarathy, Gautam (Committee member) / Trichopoulos, Georgios (Committee member) / Michelusi, Nicolò (Committee member) / Arizona State University (Publisher)
Created2024
Description
An efficient thermal solver is available in the CMC that allows modeling self-heating in the electrical simulations, which treats phonons as flux and solves the energy balance equation to quantify thermal effects. Using this solver, thermal simulations were performed on GaN-HEMTs in order to test effect of gate architectures on the DC and RF performance of the device. A Π- gate geometry is found to suppress 19.75% more hot electrons corresponding to a DC power of 2.493 W/mm for Vgs = -0.6V (max transconductance) with respect to the initial T-gate. For the DC performance, the output current, Ids is nearly same for each device configuration over the entire bias range. For the RF performance, the current gain was evaluated over a frequency range 20 GHz to 120 GHz in each device for both thermal (including self-heating) and isothermal (without self-heating). The evaluated cutoff frequency is around 7% lower for the thermal case than the isothermal case. The simulated cutoff frequency closely follows the experimental cutoff frequency. The work was extended to the study of ultra-wide bandgap material (Diamond), where isotope effect causes major deterioration in thermal conductivity. In this case, bulk phonons are modeled as semiclassical particles solving the nonlinear Peierls - Boltzmann transport equation with a stochastic approach. Simulations were performed for 0.001% (ultra-pure), 0.1% and 1.07% isotope concentration (13C) of diamond, showing good agreement with the experimental values. Further investigation was performed on the effect of isotope on the dynamics of individual phonon branches, thermal conductivity and the mean free path, to identify the dominant phonon branch. Acoustic phonons are found to be the principal contributors to thermal conductivity across all isotope concentrations with transverse acoustic (TA2) branch is the dominant branch with a contribution of 40% at room temperature and 37% at 500K. Mean free path computations show the lower bound of device dimensions in order to obtain maximum thermal conductivity. At 300K, the lowest mean free path (which is attributed to Longitudinal Optical phonon) reduces from 24nm to 8 nm for isotope concentration of 0.001% and 1.07% respectively. Similarly, the maximum mean free path (which is attributed to Longitudinal Acoustic phonon) reduces from 4 µm to 3.1 µm, respectively, for the same isotope concentrations. Furthermore, PETSc (Portable, Extensible Toolkit for Scientific Computation) developed by Argonne National Lab, was included in the existing Cellular Monte Carlo device simulator as a Poisson solver to further extend the capability of the simulator. The validity of the solver was tested performing 2D and 3D simulations and the results were compared with the well-established multigrid Poisson solver.
ContributorsAcharjee, Joy (Author) / Saraniti, Marco (Thesis advisor) / Goodnick, Stephen (Committee member) / Thornton, Trevor (Committee member) / Wang, Robert (Committee member) / Arizona State University (Publisher)
Created2024
Description
Recent advancements in communication standards, such as 5G demand transmitter hardware to support high data rates with high energy efficiency. With the revolution of communication standards, modulation schemes have become more complex and require high peak-to-average (PAPR) signals. In wireless transceiver hardware, the power amplifier (PA) consumes most of the transceiver’s DC power and is typically the bottleneck for transmitter linearity. Therefore, the transmitter’s performance directly depends on the PA. To support high PAPR signals, the PA must operate efficiently at its saturated and backoff output power. Maintaining high efficiency at both peak and backoff output power is challenging. One effective technique for addressing this problem is load modulation. Some of the prominent load-modulated PA architectures are outphasing PAs, load-modulated balanced amplifiers (LMBA), envelope elimination and restoration (EER), envelope tracking (ET), Doherty power amplifiers (DPA), and polar transmitters. Amongst them, the DPA is the most popular for infrastructure applications due to its simpler architecture compared to other techniques and linearizability with digital pre-distortion (DPD). Another crucial characteristic of progressing communication standards is wide signal bandwidths. High-efficiency power amplifiers like class J/F/F-1 and load-modulated PAs like the DPA exhibit narrowband performance because the amplifiers require precise output impedance terminations. Therefore, it is equally essential to develop adaptable PA solutions to process radio frequency (RF) signals with wide bandwidths. To support modern and future cellular infrastructure, RF PAs need to be innovated to increase the backoff power efficiency by two times or more and support ten times or more wider bandwidths than current state-of-the-art PAs. This work presents five RF PA analyses and implementations to support future wireless communications transmitter hardware. Chapter 2 presents an optimized output-matching network analysis and design to achieve extended output power backoff of the DPA. Chapters 3 and 4 unveil two bandwidth enhancement techniques for the DPA while maintaining extended output power backoff. Chapter 5 exhibits a dual-band hybrid mode PA design targeted for wideband applications. Chapter 6 presents a built-in self-test circuit integrated into a PA for output impedance monitoring. This can alleviate the PA performance degradation due to the variation in the PA's output load over frequency, process, and aging. All RF PAs in this dissertation are implemented using Gallium Nitride (GaN)-based high electron mobility transistors (HEMT), and the realized designs validate the proposed PAs' theories/architectures.
ContributorsRoychowdhury, Debatrayee (Author) / Kitchen, Jennifer (Thesis advisor) / Bakkaloglu, Bertan (Committee member) / Ozev, Sule (Committee member) / Aberle, James (Committee member) / Arizona State University (Publisher)
Created2024
Description
The world has seen a revolution in cellular communication with the advent of 5G (fifth-generation), which enables gigabits per second data speed with low latency, massive capacity, and increased availability. These modern wireless systems improve spectrum efficiency by employing advanced modulation techniques, but result in large peak-to-average power ratios (PAPR) of the transmitted signals that degrades the efficiency of the radio-frequency power amplifiers (PAs) in the power back-off (PBO) region. Envelope tracking (ET), which is a dynamic supply control technology to realize high efficiency PAs, is a promising approach for designing transmitters for the future. Conventional voltage regulators, such as linear regulators and switching regulators, fail to simultaneously offer high speed, high efficiency, and improved linearity. Hybrid supply modulators (HSM) that combine a linear and switching regulator emerge as promising solutions to achieve an optimized tradeoff between different design parameters. Over the years, considerable development and research efforts in industry and academia have been spent on maximizing HSM performance, and a majority of the most recently developed modulators are implemented in CMOS technology and mainly targeted for handset applications. In this dissertation, the main requirements for modern HSM designs are categorized and analyzed in detail. Next, techniques to improve HSM performance are discussed. The available device technologies for HSM and PA implementations are also delineated, and implementation challenges of an integrated ET-PA system are summarized. Finally, a Current-Mode with Dynamic Hysteresis HSM is proposed, designed, and implemented. With the proposed technique, the HSM is able to track LTE signals up to 100 MHz bandwidth. Switching at a peak frequency of 40 MHz, the design is able to track a 1 Vpp sinusoidal signal with high fidelity, has an output voltage ripple around 54 mV, and achieves a peak static and dynamic efficiency of 92.2% and 82.29%, respectively, at the maximum output. The HSM is capable of delivering a maximum output power of 425 mW and occupies a small die area of 1.6mm2. Overall, the proposed HSM promises competitive performance compared to state-of-the-art works.
ContributorsBHARDWAJ, SUMIT (Author) / Kitchen, Jennifer (Thesis advisor) / Ozev, Sule (Committee member) / Bakkaloglu, Bertan (Committee member) / Singh, Shrikant (Committee member) / Arizona State University (Publisher)
Created2024
Description
In the rapidly evolving field of computer vision, propelled by advancements in deeplearning, the integration of hardware-software co-design has become crucial to overcome
the limitations of traditional imaging systems. This dissertation explores the integration
of hardware-software co-design in computational imaging, particularly in light transport
acquisition and Non-Line-of-Sight (NLOS) imaging. By leveraging projector-camera
systems and computational techniques, this thesis address critical challenges in imaging
complex environments, such as adverse weather conditions, low-light scenarios, and the
imaging of reflective or transparent objects.
The first contribution in this thesis is the theory, design, and implementation of a slope
disparity gating system, which is a vertically aligned configuration of a synchronized
raster scanning projector and rolling-shutter camera, facilitating selective imaging through
disparity-based triangulation. This system introduces a novel, hardware-oriented approach
to selective imaging, circumventing the limitations of post-capture processing.
The second contribution of this thesis is the realization of two innovative approaches
for spotlight optimization to improve localization and tracking for NLOS imaging. The
first approach utilizes radiosity-based optimization to improve 3D localization and object
identification for small-scale laboratory settings. The second approach introduces a learningbased
illumination network along with a differentiable renderer and NLOS estimation
network to optimize human 2D localization and activity recognition. This approach is
validated on a large, room-scale scene with complex line-of-sight geometries and occluders.
The third contribution of this thesis is an attention-based neural network for passive
NLOS settings where there is no controllable illumination. The thesis demonstrates realtime,
dynamic NLOS human tracking where the camera is moving on a mobile robotic platform. In addition, this thesis contains an appendix featuring temporally consistent
relighting for portrait videos with applications in computer graphics and vision.
ContributorsChandran, Sreenithy (Author) / Jayasuriya, Suren (Thesis advisor) / Turaga, Pavan (Committee member) / Dasarathy, Gautam (Committee member) / Kubo, Hiroyuki (Committee member) / Arizona State University (Publisher)
Created2024
Description
Machine learning techniques have found extensive application in dynamic fields like drones, self-driving vehicles, surveillance, and more. Their effectiveness stems from meticulously crafted deep neural networks (DNNs), extensive data gathering efforts, and resource-intensive model training processes. However, due to the unpredictable nature of the environment, these systems will inevitably encounter input samples that deviate from the distribution of their original training data, resulting in instability and performance degradation.To effectively detect the emergence of out-of-distribution (OOD) data, this dissertation first proposes a novel, self-supervised approach that evaluates the Mahalanobis distance between the in-distribution (ID) and OOD in gradient space. A binary classifier is then introduced to guide the label selection for gradients calculation, which further boosts the detection performance.
Next, to continuously adapt the new OOD into the existing knowledge base, an unified framework for novelty detection and continual learning is proposed. The binary classifier, trained to distinguish OOD data from ID, is connected sequentially with the pre-trained model to form a “N + 1” classifier, where “N” represents prior knowledge which contains N classes and “1” refers to the newly arrival OOD. This continual learning process continues as “N+1+1+1+...”, assimilating the knowledge of each new OOD instance into the system.
Finally, this dissertation demonstrates the practical implementation of novelty detection and continual learning within the domain of thermal analysis. To rapidly address the impact of voids in thermal interface material (TIM), a continuous adaptation approach is proposed, which integrates trainable nodes into the graph at the locations where abnormal thermal behaviors are detected. With minimal training overhead, the model can quickly adapts to the change caused by the defects and regenerate accurate thermal prediction.
In summary, this dissertation proposes several algorithms and practical applications in continual learning aimed at enhancing the stability and adaptability of the system. All proposed algorithms are validated through extensive experiments conducted on benchmark datasets such as CIFAR-10, CIFAR-100, TinyImageNet for continual learning, and real thermal data for thermal analysis.
ContributorsSun, Jingbo (Author) / Cao, Yu (Thesis advisor) / Chhabria, Vidya (Thesis advisor) / Chakrabarti, Chaitali (Committee member) / Fan, Deliang (Committee member) / Seo, Jae-Sun (Committee member) / Arizona State University (Publisher)
Created2024
Description
In recent years, the adoption of Distributed Energy Resources (DERs) in power systems has been increasing, driven by technological advancements, development of monitoring and control techniques, policy guidance among various countries, and the benefits DERs bring to the power system. These benefits include low-cost energy production, environmental sustainability promotion, and enhanced operational efficiency of the power system. For instance, demand response (DR) can alleviate pressure during peak load periods, while solar PV units and wind turbines with smart inverters can improve grid reliability through grid regulation based on IEEE Standard 1547. Despite the opportunities DERs present, their adoption also poses challenges. The growing reliance on renewable sources introduces uncertainty, variability, and intermittency, directly impacting system stability and efficiency. Addressing these challenges necessitates comprehensive research to enhance stability, improve system operations, and maximize resource utilization. This dissertation concentrates on two primary research areas: analyzing prosumer (consumers and producers, as one) consumption behavior and developing AC optimal power flow (ACOPF) models. Firstly, understanding prosumer consumption behavior is important for reducing DERs' uncertainty, particularly DR programs. This study employs a proposed probabilistic algorithm to analyze the causal relationships between prosumer consumption behavior and other factors. Two causal-oriented approaches are utilized to establish accurate prediction models and assess demand flexibility. Causal artificial intelligence facilitates intervention and counterfactual analyses of prosumers’ DR participation and consumption behavior. Finally, a Conditional Hidden Semi-Markov Model (CHSMM) is applied to model and predict household appliance electricity consumption, further enhancing understanding of prosumer behavior. Secondly, the dissertation investigates optimization models for efficient, cost-effective power system operation and resource utilization maximization. A convex two-stage socially-aware and risk-aware Second-Order Cone Programming (SOCP)-based ACOPF model is introduced to mitigate DER uncertainty, enhance PV energy utilization, and reduce operational costs. Additionally, a convex SOCP-based ACOPF model is presented for three-phase unbalanced distribution systems, incorporating the Q-V characteristics of PV units with smart inverters based on IEEE Standard 1547. This model enables the participation of PV units with smart inverters in grid voltage regulation, enhancing power system stability and achieving efficient, cost-effective operation.
ContributorsHe, Mingyue (Author) / Khorsand, Mojdeh (Thesis advisor) / Vittal, Vijay (Committee member) / Weng, Yang (Committee member) / Wu, Meng (Committee member) / Arizona State University (Publisher)
Created2024
Description
Since the invention of the automobile, engineers have been designing and making newer and newer improvements to them in order to provide customers with safer, faster, more reliable, and more comfortable vehicles. With each new generation, new technology can be seen being introduced into mainstream products, one of which that is currently being pushed is that of autonomy. Established brand manufacturers and small research teams have been dedicated for years to find a way to make the automobile autonomous with none of them being able to confidently answer that they have found a solution. Among the engineering community there are two schools of thought when solving this issue: camera and LiDAR; some believe that only cameras and computer vision are required while other believe that LiDAR is the solution. The most optimal case is to use both cameras and LiDAR’s together in order to increase reliability and ensure data confidence. Designers are reluctant to use LiDAR systems due to their massive weight, cost, and complexity; with too many moving components, these systems are very bulky and have multiple costly, moving parts that eventually need replacement due to their constant motion. The solution to this problem is to develop a solid-state LiDAR system which would solve all those issues previously stated and this research takes it one level further and looks into a potential prototype for a solid-state camera and Lidar package. Currently no manufacturer offers a system that contains a solid-state LiDAR system and a solid-state camera with computing capabilities, all manufacturers provided either just the camera, just the Lidar, or just the computation ability. This design will also use of the shelf COTS parts in order to increase reproducibility for open-source development and to reduce total manufacturing cost. While keeping costs low, this design is also able to keep its specs and performance on par with that of a well-used commercial product, the Velodyne VL50.
ContributorsEltohamy, Gamal (Author) / Yu, Hongbin (Thesis advisor) / Goryll, Michael (Committee member) / Allee, David (Committee member) / Arizona State University (Publisher)
Created2024
Description
Humanpresence detection is essential for a various number of applications including defense and healthcare. Accurate measurements of distances, relative velocities of
humans, and other objects can be made with radars. They are largely impervious
to external factors like the impact of smoke, dust, or rain. They are also capable of
working in varied intensity of light in indoor environments. This report explores the
analyzing of real data captured and the application of different detection algorithms.
Adaptive thresholding suppresses stationary backgrounds while maintaining detection
thresholds to keep false alarm rates low. Using different approaches of Constant False
Alarm Rate (CFAR) namely Cell averaging, Smallest of Cell averaging,Greatest of Cell
Averaging and Order Statistic, this report aims to show its performance in detecting
humans in an indoor environment using real time data collected. The objective of this
project is to explain the signal processing chain of presence detection using a small
scale RADAR
ContributorsDixit, Anjali (Author) / Bliss, Daniel W (Thesis advisor) / Papandreou-Suppappola, Antonia (Committee member) / Alkhateeb, Ahmed (Committee member) / Arizona State University (Publisher)
Created2024