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- Genre: Academic theses
- Creators: Yang, Yezhou
- Member of: Theses and Dissertations
- Status: Published
Description
The partially-depleted (PD) silicon Metal Semiconductor Field Effect Transistor (MESFET) is becoming more and more attractive for analog and RF applications due to its high breakdown voltage. Compared to conventional CMOS high voltage transistors, the silicon MESFET can be fabricated in commercial standard Silicon-on-Insulator (SOI) CMOS foundries without any change to the process. The transition frequency of the device is demonstrated to be 45GHz, which makes the MESFET suitable for applications in high power RF power amplifier designs. Also, high breakdown voltage and low turn-on resistance make it the ideal choice for switches in the switching regulator designs. One of the anticipated applications of the MESFET is for the pass device for a low dropout linear regulator. Conventional NMOS and PMOS linear regulators suffer from high dropout voltage, low bandwidth and poor stability issues. In contrast, the N-MESFET pass transistor can provide an ultra-low dropout voltage and high bandwidth without the need for an external compensation capacitor to ensure stability. In this thesis, the design theory and problems of the conventional linear regulators are discussed. N-MESFET low dropout regulators are evaluated and characterized. The error amplifier used a folded cascode architecture with gain boosting. The source follower topology is utilized as the buffer to sink the gate leakage current from the MESFET. A shunt-feedback transistor is added to reduce the output impedance and provide the current adaptively. Measurement results show that the dropout voltage is less than 150 mV for a 1A load current at 1.8V output. Radiation measurements were done for discrete MESFET and fully integrated LDO regulators, which demonstrate their radiation tolerance ability for aerospace applications.
ContributorsChen, Bo (Author) / Thornton, Trevor (Thesis advisor) / Bakkaloglu, Bertan (Committee member) / Goryll, Michael (Committee member) / Arizona State University (Publisher)
Created2013
Description
The constant scaling of supply voltages in state-of-the-art CMOS processes has led to severe limitations for many analog circuit applications. Some CMOS processes have addressed this issue by adding high voltage MOSFETs to their process. Although it can be a completely viable solution, it usually requires a changing of the process flow or adding additional steps, which in turn, leads to an increase in fabrication costs. Si-MESFETs (silicon-metal-semiconductor-field-effect-transistors) from Arizona State University (ASU) on the other hand, have an inherent high voltage capability and can be added to any silicon-on-insulator (SOI) or silicon-on-sapphire (SOS) CMOS process free of cost. This has been proved at five different commercial foundries on technologies ranging from 0.5 to 0.15 μm. Another critical issue facing CMOS processes on insulated substrates is the scaling of the thin silicon channel. Consequently, the future direction of SOI/SOS CMOS transistors may trend away from partially depleted (PD) transistors and towards fully depleted (FD) devices. FD-CMOS are already being implemented in multiple applications due to their very low power capability. Since the FD-CMOS market only figures to grow, it is appropriate that MESFETs also be developed for these processes. The beginning of this thesis will focus on the device aspects of both PD and FD-MESFETs including their layout structure, DC and RF characteristics, and breakdown voltage. The second half will then shift the focus towards implementing both types of MESFETs in an analog circuit application. Aside from their high breakdown ability, MESFETs also feature depletion mode operation, easy to adjust but well controlled threshold voltages, and fT's up to 45 GHz. Those unique characteristics can allow certain designs that were previously difficult to implement or prohibitively expensive using conventional technologies to now be achieved. One such application which benefits is low dropout regulators (LDO). By utilizing an n-channel MESFET as the pass transistor, a LDO featuring very low dropout voltage, fast transient response, and stable operation can be achieved without an external capacitance. With the focus of this thesis being MESFET based LDOs, the device discussion will be mostly tailored towards optimally designing MESFETs for this particular application.
ContributorsLepkowski, William (Author) / Thornton, Trevor (Thesis advisor) / Bakkaloglu, Bertan (Committee member) / Goryll, Michael (Committee member) / Ayyanar, Raja (Committee member) / Arizona State University (Publisher)
Created2010
Description
Feature embeddings differ from raw features in the sense that the former obey certain properties like notion of similarity/dissimilarity in it's embedding space. word2vec is a preeminent example in this direction, where the similarity in the embedding space is measured in terms of the cosine similarity. Such language embedding models have seen numerous applications in both language and vision community as they capture the information in the modality (English language) efficiently. Inspired by these language models, this work focuses on learning embedding spaces for two visual computing tasks, 1. Image Hashing 2. Zero Shot Learning. The training set was used to learn embedding spaces over which similarity/dissimilarity is measured using several distance metrics like hamming / euclidean / cosine distances. While the above-mentioned language models learn generic word embeddings, in this work task specific embeddings were learnt which can be used for Image Retrieval and Classification separately.
Image Hashing is the task of mapping images to binary codes such that some notion of user-defined similarity is preserved. The first part of this work focuses on designing a new framework that uses the hash-tags associated with web images to learn the binary codes. Such codes can be used in several applications like Image Retrieval and Image Classification. Further, this framework requires no labelled data, leaving it very inexpensive. Results show that the proposed approach surpasses the state-of-art approaches by a significant margin.
Zero-shot classification is the task of classifying the test sample into a new class which was not seen during training. This is possible by establishing a relationship between the training and the testing classes using auxiliary information. In the second part of this thesis, a framework is designed that trains using the handcrafted attribute vectors and word vectors but doesn’t require the expensive attribute vectors during test time. More specifically, an intermediate space is learnt between the word vector space and the image feature space using the hand-crafted attribute vectors. Preliminary results on two zero-shot classification datasets show that this is a promising direction to explore.
Image Hashing is the task of mapping images to binary codes such that some notion of user-defined similarity is preserved. The first part of this work focuses on designing a new framework that uses the hash-tags associated with web images to learn the binary codes. Such codes can be used in several applications like Image Retrieval and Image Classification. Further, this framework requires no labelled data, leaving it very inexpensive. Results show that the proposed approach surpasses the state-of-art approaches by a significant margin.
Zero-shot classification is the task of classifying the test sample into a new class which was not seen during training. This is possible by establishing a relationship between the training and the testing classes using auxiliary information. In the second part of this thesis, a framework is designed that trains using the handcrafted attribute vectors and word vectors but doesn’t require the expensive attribute vectors during test time. More specifically, an intermediate space is learnt between the word vector space and the image feature space using the hand-crafted attribute vectors. Preliminary results on two zero-shot classification datasets show that this is a promising direction to explore.
ContributorsGattupalli, Jaya Vijetha (Author) / Li, Baoxin (Thesis advisor) / Yang, Yezhou (Committee member) / Venkateswara, Hemanth (Committee member) / Arizona State University (Publisher)
Created2019
Description
Machine learning models can pick up biases and spurious correlations from training data and projects and amplify these biases during inference, thus posing significant challenges in real-world settings. One approach to mitigating this is a class of methods that can identify filter out bias-inducing samples from the training datasets to force models to avoid being exposed to biases. However, the filtering leads to a considerable wastage of resources as most of the dataset created is discarded as biased. This work deals with avoiding the wastage of resources by identifying and quantifying the biases. I further elaborate on the implications of dataset filtering on robustness (to adversarial attacks) and generalization (to out-of-distribution samples). The findings suggest that while dataset filtering does help to improve OOD(Out-Of-Distribution) generalization, it has a significant negative impact on robustness to adversarial attacks. It also shows that transforming bias-inducing samples into adversarial samples (instead of eliminating them from the dataset) can significantly boost robustness without sacrificing generalization.
ContributorsSachdeva, Bhavdeep Singh (Author) / Baral, Chitta (Thesis advisor) / Liu, Huan (Committee member) / Yang, Yezhou (Committee member) / Arizona State University (Publisher)
Created2021
Description
There has been an explosion in the amount of data on the internet because of modern technology – especially image data – as a consequence of an exponential growth in the number of cameras existing in the world right now; from more extensive surveillance camera systems to billions of people walking around with smartphones in their pockets that come with built-in cameras. With this sudden increase in the accessibility of cameras, most of the data that is getting captured through these devices is ending up on the internet. Researchers soon took leverage of this data by creating large-scale datasets. However, generating a dataset – let alone a large-scale one – requires a lot of man-hours. This work presents an algorithm that makes use of optical flow and feature matching, along with utilizing localization outputs from a Mask R-CNN, to generate large-scale vehicle datasets without much human supervision. Additionally, this work proposes a novel multi-view vehicle dataset (MVVdb) of 500 vehicles which is also generated using the aforementioned algorithm.There are various research problems in computer vision that can leverage a multi-view dataset, e.g., 3D pose estimation, and 3D object detection. On the other hand, a multi-view vehicle dataset can be used for a 2D image to 3D shape prediction, generation of 3D vehicle models, and even a more robust vehicle make and model recognition. In this work, a ResNet is trained on the multi-view vehicle dataset to perform vehicle re-identification, which is fundamentally similar to a vehicle make and recognition problem – also showcasing the usability of the MVVdb dataset.
ContributorsGuha, Anubhab (Author) / Yang, Yezhou (Thesis advisor) / Lu, Duo (Committee member) / Banerjee, Ayan (Committee member) / Arizona State University (Publisher)
Created2022
Description
In recent years, there has been significant progress in deep learning and computer vision, with many models proposed that have achieved state-of-art results on various image recognition tasks. However, to explore the full potential of the advances in this field, there is an urgent need to push the processing of deep networks from the cloud to edge devices. Unfortunately, many deep learning models cannot be efficiently implemented on edge devices as these devices are severely resource-constrained. In this thesis, I present QU-Net, a lightweight binary segmentation model based on the U-Net architecture. Traditionally, neural networks consider the entire image to be significant. However, in real-world scenarios, many regions in an image do not contain any objects of significance. These regions can be removed from the original input allowing a network to focus on the relevant regions and thus reduce computational costs. QU-Net proposes the salient regions (binary mask) that the deeper models can use as the input. Experiments show that QU-Net helped achieve a computational reduction of 25% on the Microsoft Common Objects in Context (MS COCO) dataset and 57% on the Cityscapes dataset. Moreover, QU-Net is a generalizable model that outperforms other similar works, such as Dynamic Convolutions.
ContributorsSanthosh Kumar Varma, Rahul (Author) / Yang, Yezhou (Thesis advisor) / Fan, Deliang (Committee member) / Yang, Yingzhen (Committee member) / Arizona State University (Publisher)
Created2021
Description
Retinotopic map, the map between visual inputs on the retina and neuronal activation in brain visual areas, is one of the central topics in visual neuroscience. For human observers, the map is typically obtained by analyzing functional magnetic resonance imaging (fMRI) signals of cortical responses to slowly moving visual stimuli on the retina. Biological evidences show the retinotopic mapping is topology-preserving/topological (i.e. keep the neighboring relationship after human brain process) within each visual region.
Unfortunately, due to limited spatial resolution and the signal-noise ratio of fMRI, state of art retinotopic map is not topological.
The topic was to model the topology-preserving condition mathematically, fix non-topological retinotopic map with numerical methods, and improve the quality of retinotopic maps.
The impose of topological condition, benefits several applications.
With the topological retinotopic maps, one may have a better insight on human retinotopic maps, including better cortical magnification factor quantification, more precise description of retinotopic maps, and potentially better exam ways of in Ophthalmology clinic.
ContributorsTu, Yanshuai (Author) / Wang, Yalin (Thesis advisor) / Lu, Zhong-Lin (Committee member) / Crook, Sharon (Committee member) / Yang, Yezhou (Committee member) / Zhang, Yu (Committee member) / Arizona State University (Publisher)
Created2022
Description
Multimodal reasoning is one of the most interesting research fields because of the ability to interact with systems and the explainability of the models' behavior. Traditional multimodal research problems do not focus on complex commonsense reasoning (such as physical interactions). Although real-world objects have physical properties associated with them, many of these properties (such as mass and coefficient of friction) are not captured directly by the imaging pipeline. Videos often capture objects, their motion, and the interactions between different objects. However, these properties can be estimated by utilizing cues from relative object motion and the dynamics introduced by collisions. This thesis introduces a new video question-answering task for reasoning about the implicit physical properties of objects in a scene, from videos. For this task, I introduce a dataset -- CRIPP-VQA (Counterfactual Reasoning about Implicit Physical Properties - Video Question Answering), which contains videos of objects in motion, annotated with hypothetical/counterfactual questions about the effect of actions (such as removing, adding, or replacing objects), questions about planning (choosing actions to perform to reach a particular goal), as well as descriptive questions about the visible properties of objects. Further, I benchmark the performance of existing video question-answering models on two test settings of CRIPP-VQA: i.i.d. and an out-of-distribution setting which contains objects with values of mass, coefficient of friction, and initial velocities that are not seen in the training distribution. Experiments reveal a surprising and significant performance gap in terms of answering questions about implicit properties (the focus of this thesis) and explicit properties (the focus of prior work) of objects.
ContributorsPatel, Maitreya Jitendra (Author) / Yang, Yezhou (Thesis advisor) / Baral, Chitta (Committee member) / Lee, Kookjin (Committee member) / Arizona State University (Publisher)
Created2022
Description
To date, there is not a standardized method for consistently quantifying the performance of an automated driving system (ADS)-equipped vehicle (AV). The purpose of this dissertation is to contribute to a framework for such an approach referred to throughout as the operational safety assessment (OSA) methodology. Through this research, safety metrics are identified, researched, and analyzed to capture aspects of the operational safety of AVs, interacting with other salient objects. This dissertation outlines the approach for developing this methodology through a series of key steps including: (1) comprehensive literature review; (2) research and refinement of OSA metrics; (3) generation of MATLAB script for metric calculations; (4) generation of simulated events for analysis; (5) collection of real-world data for analysis; (6) review of OSA methodology results; and (7) discussion of future work to expand complexity, fidelity, and relevance aspects of the OSA methodology. The detailed literature review includes the identification of metrics historically used in both traditional and more recent evaluations of vehicle performance. Subsequently, the metric formulations are refined, and robust severity evaluations are proposed. A MATLAB script is then presented which was generated to calculate the metrics from any given source assuming proper formatting of the data. To further refine the formulations and the MATLAB script, a variety of simulated scenarios are discussed including car-following, intersection, and lane change situations. Additionally, a data collection activity is presented, leveraging the SMARTDRIVE testbed operated by Maricopa County Department of Transportation in Anthem, AZ to collect real-world data from an active intersection. Lastly, the efficacy of the OSA methodology with respect to the evaluation of vehicle performance for a set of scenarios is evaluated utilizing both simulated and real-world data. This assessment provides a demonstration of the ability and robustness of this methodology to evaluate vehicle performance for a given scenario. At the conclusion of this dissertation, additional factors including fidelity, complexity, and relevance are explored to contribute to a more comprehensive evaluation.
ContributorsComo, Steven Gerard (Author) / Wishart, Jeffrey (Thesis advisor) / Yang, Yezhou (Thesis advisor) / Chen, Yan (Committee member) / Favaro, Francesca (Committee member) / Arizona State University (Publisher)
Created2022
Description
In natural language processing, language models have achieved remarkable success over the last few years. The Transformers are at the core of most of these models. Their success can be mainly attributed to an enormous amount of curated data they are trained on. Even though such language models are trained on massive curated data, they often need specific extracted knowledge to understand better and reason. This is because often relevant knowledge may be implicit or missing, which hampers machine reasoning. Apart from that, manual knowledge curation is time-consuming and erroneous. Hence, finding fast and effective methods to extract such knowledge from data is important for improving language models. This leads to finding ideal ways to utilize such knowledge by incorporating them into language models. Successful knowledge extraction and integration lead to an important question of knowledge evaluation of such models by developing tools or introducing challenging test suites to learn about their limitations and improve them further. So to improve the transformer-based models, understanding the role of knowledge becomes important. In the pursuit to improve language models with knowledge, in this dissertation I study three broad research directions spanning across the natural language, biomedical and cybersecurity domains: (1) Knowledge Extraction (KX) - How can transformer-based language models be leveraged to extract knowledge from data? (2) Knowledge Integration (KI) - How can such specific knowledge be used to improve such models? (3) Knowledge Evaluation (KE) - How can language models be evaluated for specific skills and understand their limitations?
I propose methods to extract explicit textual, implicit structural, missing textual, and missing structural knowledge from natural language and binary programs using transformer-based language models. I develop ways to improve the language model’s multi-step and commonsense reasoning abilities using external knowledge. Finally, I develop challenging datasets which assess their numerical reasoning skills in both in-domain and out-of-domain settings.
ContributorsPal, Kuntal Kumar (Author) / Baral, Chitta (Thesis advisor) / Wang, Ruoyu (Committee member) / Blanco, Eduardo (Committee member) / Yang, Yezhou (Committee member) / Arizona State University (Publisher)
Created2023