Matching Items (131)
Description
The development of portable electronic systems has been a fundamental factor to the emergence of new applications including ubiquitous smart devices, self-driving vehicles. Power-Management Integrated Circuits (PMICs) which are a key component of such systems must maintain high efficiency and reliability for the final system to be appealing from a size and cost perspective. As technology advances, such portable systems require high output currents at low voltages from their PMICs leading to thermal reliability concerns. The reliability and power integrity of PMICs in such systems also degrades when operated in harsh environments. This dissertation presents solutions to solve two such reliability problems.The first part of this work presents a scalable, daisy-chain solution to parallelize multiple low-dropout linear (LDO) regulators to increase the total output current at low voltages. This printed circuit board (PCB) friendly approach achieves output current sharing without the need for any off-chip active or passive components or matched PCB traces thus reducing the overall system cost. Fully integrated current sensing based on dynamic element matching eliminates the need for any off-chip current sensing components. A current sharing accuracy of 2.613% and 2.789% for output voltages of 3V and 1V respectively and an output current of 2A per LDO are measured for the parallel LDO system implemented in a 0.18μm process. Thermal images demonstrate that the parallel LDO system achieves thermal equilibrium and stable reliable operation.
The remainder of the thesis deals with time-domain switching regulators for high-reliability applications. A time-domain based buck and boost controller with time as the processing variable is developed for use in harsh environments. The controller features adaptive on-time / off-time generation for quasi-constant switching frequency and a time-domain comparator to implement current-mode hysteretic control. A triple redundant bandgap reference is also developed to mitigate the effects of radiation. Measurement results are showcased for a buck and boost converter with a common controller IC implemented in a 0.18μm process and an external power stage. The converter achieves a peak efficiency of 92.22% as a buck for an output current of 5A and an output voltage of 5V. Similarly, the converter achieves an efficiency of 95.97% as a boost for an output current of 1.25A and an output voltage of 30.4V.
ContributorsTalele, Bhushan (Author) / Bakkaloglu, Bertan (Thesis advisor) / Garrity, Douglas (Committee member) / Seo, Jae-Sun (Committee member) / Kitchen, Jennifer (Committee member) / Arizona State University (Publisher)
Created2021
Description
The rapid growth of Internet-of-things (IoT) and artificial intelligence applications have called forth a new computing paradigm--edge computing. Edge computing applications, such as video surveillance, autonomous driving, and augmented reality, are highly computationally intensive and require real-time processing. Current edge systems are typically based on commodity general-purpose hardware such as Central Processing Units (CPUs) and Graphical Processing Units (GPUs) , which are mainly designed for large, non-time-sensitive jobs in the cloud and do not match the needs of the edge workloads. Also, these systems are usually power hungry and are not suitable for resource-constrained edge deployments. Such application-hardware mismatch calls forth a new computing backbone to support the high-bandwidth, low-latency, and energy-efficient requirements. Also, the new system should be able to support a variety of edge applications with different characteristics. This thesis addresses the above challenges by studying the use of Field Programmable Gate Array (FPGA) -based computing systems for accelerating the edge workloads, from three critical angles. First, it investigates the feasibility of FPGAs for edge computing, in comparison to conventional CPUs and GPUs. Second, it studies the acceleration of common algorithmic characteristics, identified as loop patterns, using FPGAs, and develops a benchmark tool for analyzing the performance of these patterns on different accelerators. Third, it designs a new edge computing platform using multiple clustered FPGAs to provide high-bandwidth and low-latency acceleration of convolutional neural networks (CNNs) widely used in edge applications. Finally, it studies the acceleration of the emerging neural networks, randomly-wired neural networks, on the multi-FPGA platform. The experimental results from this work show that the new generation of workloads requires rethinking the current edge-computing architecture. First, through the acceleration of common loops, it demonstrates that FPGAs can outperform GPUs in specific loops types up to 14 times. Second, it shows the linear scalability of multi-FPGA platforms in accelerating neural networks. Third, it demonstrates the superiority of the new scheduler to optimally place randomly-wired neural networks on multi-FPGA platforms with 81.1 times better throughput than the available scheduling mechanisms.
ContributorsBiookaghazadeh, Saman (Author) / Zhao, Ming (Thesis advisor) / Ren, Fengbo (Thesis advisor) / Li, Baoxin (Committee member) / Seo, Jae-Sun (Committee member) / Arizona State University (Publisher)
Created2021
Description
Floating trash objects are very commonly seen on water bodies such as lakes, canals and rivers. With the increase of plastic goods and human activities near the water bodies, these trash objects can pile up and cause great harm to the surrounding environment. Using human workers to clear out these trash is a hazardous and time-consuming task. Employing autonomous robots for these tasks is a better approach since it is more efficient and faster than humans. However, for a robot to clean the trash objects, a good detection algorithm is required. Real-time object detection on water surfaces is a challenging issue due to nature of the environment and the volatility of the water surface. In addition to this, running an object detection algorithm on an on-board processor of a robot limits the amount of CPU consumption that the algorithm can utilize. In this thesis, a computationally low cost object detection approach for robust detection of trash objects that was run on an on-board processor of a multirotor is presented. To account for specular reflections on the water surface, we use a polarization filter and integrate a specularity removal algorithm on our approach as well. The challenges faced during testing and the means taken to eliminate those challenges are also discussed. The algorithm was compared with two other object detectors using 4 different metrics. The testing was carried out using videos of 5 different objects collected at different illumination conditions over a lake using a multirotor. The results indicate that our algorithm is much suitable to be employed in real-time since it had the highest processing speed of 21 FPS, the lowest CPU consumption of 37.5\% and considerably high precision and recall values in detecting the object.
ContributorsSyed, Danish Faraaz (Author) / Zhang, Wenlong (Thesis advisor) / Yang, Yezhou (Committee member) / Turaga, Pavan (Committee member) / Arizona State University (Publisher)
Created2021
Description
Unmanned aerial vehicles (UAVs) have reshaped the world of aviation. With the emergence of different types of UAVs, a multitude of mission critical applications, e.g., aerial photography, package delivery, grasping and manipulation, aerial reconnaissance and surveillance have been accomplished successfully. All of the aforementioned applications require the UAVs to be robust to external disturbances and safe while flying in cluttered environments and these factors are of paramount importance for task completion. In the first phase, this dissertation starts by presenting the synthesis and experimental validation of real-time low-level estimation and robust attitude and position controllers for multirotors. For the task of reliable position estimation, a hybrid low-pass de-trending filter is proposed for attenuating noise and drift in the velocity and position estimates respectively. Subsequently, a disturbance observer (DOB) approach with online Q-filter tuning is proposed for disturbance rejection and precise position control. Finally, a non-linear disturbance observer (NDOB) approach, along with a parameter optimization framework, is proposed for robust attitude control of multirotors. Multiple simulation and experimental flight tests are performed to demonstrate the efficacy of the proposed algorithms. Aerial grasping and collection is a type of mission-critical task which requires vision based sensing and robust control algorithms for successful task completion. In the second phase, this dissertation initially explores different object grasping approaches utilizing soft and rigid graspers. Additionally, vision based control paradigms are developed for object grasping and collection applications, specifically from water surfaces. Autonomous object collection from water surfaces presents a multitude of challenges: i) object drift due to propeller outwash, ii) reflection and glare from water surfaces makes object detection extremely challenging and iii) lack of reliable height sensors above water surface (for autonomous landing on water). Finally, a first of its kind aerial manipulation system, with an integrated net system and a robust vision based control structure, is proposed for floating object collection from water surfaces. Objects of different shapes and sizes are collected, through multiple experimental flight tests, with a success rate of 91.6%. To the best of the author's knowledge, this is the first work demonstrating autonomous object collection from water surfaces.
ContributorsMishra, Shatadal (Author) / Zhang, Wenlong (Thesis advisor) / Berman, Spring M (Committee member) / Sugar, Thomas G (Committee member) / Arizona State University (Publisher)
Created2021
Description
Stiffness and flexibility are essential in many fields, including robotics, aerospace, bioengineering, etc. In recent years, origami-based mechanical metamaterials were designed for better mechanical properties including tunable stiffness and tunable collapsibility. However, in existing studies, the tunable stiffness is only with limited range and limited controllability. To overcome these challenges, two objectives were proposed and achieved in this dissertation: first, to design mechanical metamaterials with metamaterials with selective stiffness and collapsibility; second, to design mechanical metamaterials with in-situ tunable stiffness among positive, zero, and negative.In the first part, triangulated cylinder origami was employed to build deployable mechanical metamaterials through folding and unfolding along the crease lines. These deployable structures are flexible in the deploy direction so that it can be easily collapsed along the same way as it was deployed. An origami-inspired mechanical metamaterial was designed for on-demand deployability and selective collapsibility: autonomous deployability from the collapsed state and selective collapsibility along two different paths, with low stiffness for one path and substantially high stiffness for another path. The created mechanical metamaterial yields unprecedented load bearing capability in the deploy direction while possessing great deployability and collapsibility. The principle in this prospectus can be utilized to design and create versatile origami-inspired mechanical metamaterials that can find many applications.
In the second part, curved origami patterns were designed to accomplish in situ stiffness manipulation covering positive, zero, and negative stiffness by activating predefined creases on one curved origami pattern. This elegant design enables in situ stiffness switching in lightweight and space-saving applications, as demonstrated through three robotic-related components. Under a uniform load, the curved origami can provide universal gripping, controlled force transmissibility, and multistage stiffness response. This work illustrates an unexplored and unprecedented capability of curved origami, which opens new applications in robotics for this particular family of origami patterns.
ContributorsZhai, Zirui (Author) / Nian, Qiong (Thesis advisor) / Zhuang, Houlong (Committee member) / Huang, Huei-Ping (Committee member) / Zhang, Wenlong (Committee member) / Liu, Yongming (Committee member) / Arizona State University (Publisher)
Created2021
Description
Walking and mobility are essential aspects of our daily lives, enabling us to engage in various activities. Gait disorders and impaired mobility are widespread challenges faced by older adults and people with neurological injuries, as these conditions can significantly impact their quality of life, leading to a loss of independence and an increased risk of mortality. In response to these challenges, rehabilitation, and assistive robotics have emerged as promising alternatives to conventional gait therapy, offering potential solutions that are less labor-intensive and costly. Despite numerous advances in wearable lower-limb robotics, their current applicability remains confined to laboratory settings. To expand their utility to broader gait impairments and daily living conditions, there is a pressing need for more intelligent robot controllers. In this dissertation, these challenges are tackled from two perspectives: First, to improve the robot's understanding of human motion and intentions which is crucial for assistive robot control, a robust human locomotion estimation technique is presented, focusing on measuring trunk motion. Employing an invariant extended Kalman filtering method that takes sensor misplacement into account, improved convergence properties over the existing methods for different locomotion modes are shown. Secondly, to enhance safe and effective robot-aided gait training, this dissertation proposes to directly learn from physical therapists' demonstrations of manual gait assistance in post-stroke rehabilitation.
Lower-limb kinematics of patients and assistive force applied by therapists to the patient's leg are measured using a wearable sensing system which includes a custom-made force sensing array. The collected data is then used to characterize a therapist's strategies. Preliminary analysis indicates that knee extension and weight-shifting play pivotal roles in shaping a therapist's assistance strategies, which are then incorporated into a virtual impedance model that effectively captures high-level therapist behaviors throughout a complete training session. Furthermore, to introduce safety constraints in the design of such controllers, a safety-critical learning framework is explored through theoretical analysis and simulations. A safety filter incorporating an online iterative learning component is introduced to bring robust safety guarantees for gait robotic assistance and training, addressing challenges such as stochasticity and the absence of a known prior dynamic model.
ContributorsRezayat Sorkhabadi, Seyed Mostafa (Author) / Zhang, Wenlong (Thesis advisor) / Berman, Spring (Committee member) / Lee, Hyunglae (Committee member) / Marvi, Hamid (Committee member) / Sugar, Thomas (Committee member) / Arizona State University (Publisher)
Created2023
Description
The field of unmanned aerial vehicle, or UAV, navigation has been moving towards collision inclusive path planning, yet work has not been done to consider what a UAV is colliding with, and if it should or not. Therefore, there is a need for a framework that allows a UAV to consider what is around it and find the best collision candidate. The following work presents a framework that allows UAVs to do so, by considering what an object is and the properties associated with it. Specifically, it considers an object’s material and monetary value to decide if it is good to collide with or not. This information is then published on a binary occupancy map that contains the objects’ size and location with respect to the current position of the UAV. The intent is that the generated binary occupancy map can be used with a path planner to decide what the UAV should collide with. The framework was designed to be as modular as possible and to work with conventional UAV's that have some degree of crash resistance incorporated into their design. The framework was tested by using it to identify various objects that could be collision candidates or not, and then carrying out collisions with some of the objects to test the framework’s accuracy. The purpose of this research was to further the field of collision inclusive path planning by allowing UAVs to know, in a way, what they are intending to collide with and decide if they should or not in order to make safer and more efficient collisions.
ContributorsMolnar, Madelyn Helena (Author) / Zhang, Wenlong (Thesis advisor) / Sugar, Thomas (Committee member) / Guo, Shenghan (Committee member) / Arizona State University (Publisher)
Created2024
Description
Physical inactivity is a major contributor to chronic illnesses and mortality globally. However, most interventions to address it rely on static, aggregate models that overlook idiographic (i.e., individual-level) dynamics, limiting intervention effectiveness. Leveraging mobile technology and control systems engineering principles, this dissertation provides a novel, comprehensive framework for personalized behavioral interventions that have been tested experimentally under the Control Optimization Trial (COT) paradigm. Through careful design of experiments, elaborate signal processing and model estimation, and judicious formulation of behavior intervention optimization as a control system problem, this dissertation develops tools to overcome challenges faced in the large-scale dissemination of mobile health (mHealth) interventions. A novel Three-Degrees-of-Freedom Kalman Filter-based Hybrid Model Predictive Control (3DoF-KF HMPC) controller is formulated for physical activity interventions and evaluated in a clinical trial, demonstrating its effectiveness. Furthermore, this dissertation expands on understanding the underlying dynamics influencing behavior change. Engineering principles are applied to develop a conceptual approach to generate dynamic hypotheses and translate these into first-principle dynamic models. The generated models are used in concert with system identification principles to enhance the design of experiments that yield dynamically informative data sets for behavioral medicine applications. Additionally, sophisticated search, filtering, and model estimation algorithms are applied to optimize and personalize model structures and estimate dynamic models that account for nonlinearities and “Just-in-Time” (JIT; moments of need, receptivity, and opportunity) context in behavior change systems. In addition, the pervasive issue of data missingness in interventions is addressed by integrating system identification principles with a Bayesian inference model-based technique for data imputation. The findings in this dissertation extend beyond physical activity, offering insights for promoting healthy behaviors in other applications, such as smoking cessation and weight management. The integration of control systems engineering in behavioral medicine research, as demonstrated in this dissertation, offers broad impacts by advancing the field's understanding of behavior change dynamics, enhancing accessibility to personalized behavioral health interventions, and improving patient outcomes. This research has the potential to radically improve behavioral interventions, increase affordability and accessibility, inspire interdisciplinary collaboration, and provide behavioral scientists with tools capable of addressing societal challenges in mHealth and preventive medicine.
ContributorsEl Mistiri, Mohamed (Author) / Rivera, Daniel E. (Thesis advisor) / Deng, Shuguang (Committee member) / Muhich, Christopher (Committee member) / Pavlic, Theodore P. (Committee member) / Zhang, Wenlong (Committee member) / Arizona State University (Publisher)
Created2024
Description
Human-robot interactions can often be formulated as general-sum differential games where the equilibrial policies are governed by Hamilton-Jacobi-Isaacs (HJI) equations. Solving HJI PDEs faces the curse of dimensionality (CoD). While physics-informed neural networks (PINNs) alleviate CoD in solving PDEs with smooth solutions, they fall short in learning discontinuous solutions due to their sampling nature. This causes PINNs to have poor safety performance when they are applied to approximate values that are discontinuous due to state constraints. This dissertation aims to improve the safety performance of PINN-based value and policy models. The first contribution of the dissertation is to develop learning methods to approximate discontinuous values. Specifically, three solutions are developed: (1) hybrid learning uses both supervisory and PDE losses, (2) value-hardening solves HJIs with increasing Lipschitz constant on the constraint violation penalty, and (3) the epigraphical technique lifts the value to a higher-dimensional state space where it becomes continuous. Evaluations through 5D and 9D vehicle and 13D drone simulations reveal that the hybrid method outperforms others in terms of generalization and safety performance. The second contribution is a learning-theoretical analysis of PINN for value and policy approximation. Specifically, by extending the neural tangent kernel (NTK) framework, this dissertation explores why the choice of activation function significantly affects the PINN generalization performance, and why the inclusion of supervisory costate data improves the safety performance. The last contribution is a series of extensions of the hybrid PINN method to address real-time parameter estimation problems in incomplete-information games. Specifically, a Pontryagin-mode PINN is developed to avoid costly computation for supervisory data. The key idea is the introduction of a costate loss, which is cheap to compute yet effectively enables the learning of important value changes and policies in space-time. Building upon this, a Pontryagin-mode neural operator is developed to achieve state-of-the-art (SOTA) safety performance across a set of differential games with parametric state constraints. This dissertation demonstrates the utility of the resultant neural operator in estimating player constraint parameters during incomplete-information games.
ContributorsZhang, Lei (Author) / Ren, Yi (Thesis advisor) / Si, Jennie (Committee member) / Berman, Spring (Committee member) / Zhang, Wenlong (Committee member) / Xu, Zhe (Committee member) / Arizona State University (Publisher)
Created2024
Description
Resistive random-access memory (RRAM) or memristor, is an emerging technology used in neuromorphic computing to exceed the traditional von Neumann obstacle by merging the processing and memory units. Two-dimensional (2D) materials with non-volatile switching behavior can be used as the switching layer of RRAMs, exhibiting superior behavior compared to conventional oxide-based RRAMs. The use of 2D materials allows scaling the resistive switching layer thickness to sub-nanometer dimensions enabling devices to operate with low switching voltages and high programming speeds, offering large improvements in efficiency and performance as well as ultra-dense integration. This dissertation presents an extensive study of linear and logistic regression algorithms implemented with 1-transistor-1-resistor (1T1R) memristor crossbars arrays. For this task, a simulation platform is used that wraps circuit-level simulations of 1T1R crossbars and physics-based model of RRAM to elucidate the impact of device variability on algorithm accuracy, convergence rate, and precision. Moreover, a smart pulsing strategy is proposed for the practical implementation of synaptic weight updates that can accelerate training in real crossbar architectures.
Next, this dissertation reports on the hardware implementation of analog dot-product operation on arrays of 2D hexagonal boron nitride (h-BN) memristors. This extends beyond previous work that studied isolated device characteristics towards the application of analog neural network accelerators based on 2D memristor arrays. The wafer-level fabrication of the memristor arrays is enabled by large-area transfer of CVD-grown few-layer h-BN films. The dot-product operation shows excellent linearity and repeatability, with low read energy consumption, with minimal error and deviation over various measurement cycles. Moreover, the successful implementation of a stochastic linear and logistic regression algorithm in 2D h-BN memristor hardware is presented for the classification of noisy images. Additionally, the electrical performance of novel 2D h-BN memristor for SNN applications is extensively investigated. Then, using the experimental behavior of the h-BN memristor as the artificial synapse, an unsupervised spiking neural network (SNN) is simulated for the image classification task. A novel and simple Spike-Timing-Dependent-Plasticity (STDP)-based dropout technique is presented to enhance the recognition task of the h-BN memristor-based SNN.
ContributorsAfshari, Sahra (Author) / Sanchez Esqueda, Ivan (Thesis advisor) / Barnaby, Hugh J (Committee member) / Seo, Jae-Sun (Committee member) / Cao, Yu (Committee member) / Arizona State University (Publisher)
Created2023