Matching Items (247)
Description
Impedance is one of the fundamental properties of electrical components, materials, and waves. Therefore, impedance measurement and monitoring have a wide range of applications. The multi-port technique is a natural candidate for impedance measurement and monitoring due to its low overhead and ease of implementation for Built-in Self-Test (BIST) applications. The multi-port technique can measure complex reflection coefficients, thus impedance, by using scalar measurements provided by the power detectors. These power detectors are strategically placed on different points (ports) of a passive network to produce unique solution. Impedance measurement and monitoring is readily deployed on mobile phone radio-frequency (RF) front ends, and are combined with antenna tuners to boost the signal reception capabilities of phones. These sensors also can be used in self-healing circuits to improve their yield and performance under process, voltage, and temperature variations. Even though, this work is preliminary interested in low-overhead impedance measurement for RF circuit applications, the proposed methods can be used in a wide variety of metrology applications where impedance measurements are already used. Some examples of these applications include determining material properties, plasma generation, and moisture detection. Additionally, multi-port applications extend beyond the impedance measurement. There are applications where multi-ports are used as receivers for communication systems, RADARs, and remote sensing applications. The multi-port technique generally requires a careful design of the testing structure to produce a unique solution from power detector measurements. It also requires the use of nonlinear solvers during calibration, and depending on calibration procedure, measurement. The use of nonlinear solvers generates issues for convergence, computational complexity, and resources needed for carrying out calibrations and measurements in a timely manner. In this work, using periodic structures, a structure where a circuit block repeats itself, for multi-port measurements is proposed. The periodic structures introduce a new constraint that simplifies the multi-port theory and leads to an explicit calibration and measurement procedure. Unlike the existing calibration procedures which require at least five loads and various constraints on the load for explicit solution, the proposed method can use three loads for calibration. Multi-ports built with periodic structures will always produce a unique measurement result. This leads to increased bandwidth of operation and simplifies design procedure. The efficacy of the method demonstrated in two embodiments.
In the first embodiment, a multi-port is directly embedded into a matching network to measure impedance of the load.
In the second embodiment, periodic structures are used to compare two loads without requiring any calibration.
ContributorsAvci, Muslum Emir (Author) / Ozev, Sule (Thesis advisor) / Bakkaloglu, Bertan (Committee member) / Kitchen, Jennifer (Committee member) / Trichopoulos, Georgios (Committee member) / Arizona State University (Publisher)
Created2023
Description
Modern-day automobiles are becoming more connected and reliant on wireless connectivity. Thus, automotive electronics can be both a cause of and highly sensitive to electromagnetic interference (EMI), and the consequences of failure can be fatal. Technology advancements in engineering have brought several features into the automotive field but at the expense of electromagnetic compatibility issues. Automotive EMC problems are the result of the emissions from electronic assemblies inside a vehicle and the susceptibility of the electronics when exposed to external EMI sources. In both cases, automotive EMC problems can cause unintended changes in the automotive system operation. Robustness to electromagnetic interference (EMI) is one of the primary design aspects of state-of-the-art automotive ICs like System Basis Chips (SBCs) which provide a wide range of analog, power regulation and digital functions on the same die. One of the primary sources of conducted EMI on the Local Interconnect Network (LIN) driver output is an integrated switching DC-DC regulator noise coupling through the parasitic substrate capacitance of the SBC. In this dissertation an adaptive active EMI cancellation technique to cancel the switching noise of the DC-DC regulator on the LIN driver output to ensure electromagnetic compatibility (EMC) is presented. The proposed active EMI cancellation circuit synthesizes a phase synchronized cancellation pulse which is then injected onto the LIN driver output using an on-chip tunable capacitor array to cancel the switching noise injected via the substrate. The proposed EMI reduction technique can track and cancel substrate noise independent of process technology and device parasitics, input voltage, duty cycle, and loading conditions of the DC-DC switching regulator. The EMI cancellation system is designed and fabricated on a 180nm Bipolar-CMOS-DMOS (BCD) process with an integrated power stage of a DC-DC buck regulator at a switching frequency of 2MHz along with an automotive LIN driver. The EMI cancellation circuit occupies an area of 0.7 mm2, which is less than 3% of the overall area in a standard SBC and consumes 12.5 mW of power and achieves 25 dB reduction of conducted EMI in the LIN driver output’s power spectrum at the switching frequency and its harmonics.
ContributorsRay, Abhishek (Author) / Bakkaloglu, Bertan (Thesis advisor) / Garrity, Douglas (Committee member) / Kitchen, Jennifer (Committee member) / Seo, Jae-Sun (Committee member) / Arizona State University (Publisher)
Created2023
Description
Stress, depression, and anxiety are prevailing mental health issues that affect individuals worldwide. As the search for effective solutions continues, advancements in technology have led to the development of digital tools for stress identification and management purposes. The Cigna StressWaves Test (CSWT) is a publicly available stress analysis toolkit that claims to use “clinical-grade” artificial intelligence (AI) technology to evaluate individual stress levels through speech. To investigate their claim, this research stands as an independent validation study involving 60 participants over the age of 18. The primary objective of the study is to assess the repeatability and efficacy of the CSWT as a stress measurement tool. Key results indicate the CSWT lacks test-retest reliability and convergent validity. This implies that the CWST is not a repeatable tool and does not provide similar stress outcomes relative to an established measure of stress, the Perceived Stress Scale (PSS). These findings cast doubt on the accuracy and effectiveness of the CWST as a stress assessment tool. The public availability of the CSWT and the claim that it is a “clinical-grade” tool highlights concerns regarding premature deployment of digital health tools for stress management.
ContributorsYawer, Batul (Author) / Berisha, Visar (Thesis advisor) / Liss, Julie (Committee member) / Luo, Xin (Committee member) / Arizona State University (Publisher)
Created2023
Description
Linear-regression estimators have become widely accepted as a reliable statistical tool in predicting outcomes. Because linear regression is a long-established procedure, the properties of linear-regression estimators are well understood and can be trained very quickly. Many estimators exist for modeling linear relationships, each having ideal conditions for optimal performance. The differences stem from the introduction of a bias into the parameter estimation through the use of various regularization strategies. One of the more popular ones is ridge regression which uses ℓ2-penalization of the parameter vector. In this work, the proposed graph regularized linear estimator is pitted against the popular ridge regression when the parameter vector is known to be dense. When additional knowledge that parameters are smooth with respect to a graph is available, it can be used to improve the parameter estimates. To achieve this goal an additional smoothing penalty is introduced into the traditional loss function of ridge regression. The mean squared error(m.s.e) is used as a performance metric and the analysis is presented for fixed design matrices having a unit covariance matrix. The specific problem setup enables us to study the theoretical conditions where the graph regularized estimator out-performs the ridge estimator. The eigenvectors of the laplacian matrix indicating the graph of connections between the various dimensions of the parameter vector form an integral part of the analysis. Experiments have been conducted on simulated data to compare the performance of the two estimators for laplacian matrices of several types of graphs – complete, star, line and 4-regular. The experimental results indicate that the theory can possibly be extended to more general settings taking smoothness, a concept defined in this work, into consideration.
ContributorsSajja, Akarshan (Author) / Dasarathy, Gautam (Thesis advisor) / Berisha, Visar (Committee member) / Yang, Yingzhen (Committee member) / Arizona State University (Publisher)
Created2022
Description
In the standard pipeline for machine learning model development, several design decisions are made largely based on trial and error. Take the classification problem as an example. The starting point for classifier design is a dataset with samples from the classes of interest. From this, the algorithm developer must decide which features to extract, which hypothesis class to condition on, which hyperparameters to select, and how to train the model. The design process is iterative with the developer trying different classifiers, feature sets, and hyper-parameters and using cross-validation to pick the model with the lowest error. As there are no guidelines for when to stop searching, developers can continue "optimizing" the model to the point where they begin to "fit to the dataset". These problems are amplified in the active learning setting, where the initial dataset may be unlabeled and label acquisition is costly. The aim in this dissertation is to develop algorithms that provide ML developers with additional information about the complexity of the underlying problem to guide downstream model development. I introduce the concept of "meta-features" - features extracted from a dataset that characterize the complexity of the underlying data generating process. In the context of classification, the complexity of the problem can be characterized by understanding two complementary meta-features: (a) the amount of overlap between classes, and (b) the geometry/topology of the decision boundary. Across three complementary works, I present a series of estimators for the meta-features that characterize overlap and geometry/topology of the decision boundary, and demonstrate how they can be used in algorithm development.
ContributorsLi, Weizhi (Author) / Berisha, Visar (Thesis advisor) / Dasarathy, Gautam (Thesis advisor) / Natesan Ramamurthy, Karthikeyan (Committee member) / Turaga, Pavan (Committee member) / Arizona State University (Publisher)
Created2022
Description
Severe forms of mental illness, such as schizophrenia and bipolar disorder, are debilitating conditions that negatively impact an individual's quality of life. Additionally, they are often difficult and expensive to diagnose and manage, placing a large burden on society. Mental illness is typically diagnosed by the use of clinical interviews and a set of neuropsychiatric batteries; a key component of nearly all of these evaluations is some spoken language task. Clinicians have long used speech and language production as a proxy for neurological health, but most of these assessments are subjective in nature. Meanwhile, technological advancements in speech and natural language processing have grown exponentially over the past decade, increasing the capacity of computer models to assess particular aspects of speech and language. For this reason, many have seen an opportunity to leverage signal processing and machine learning applications to objectively assess clinical speech samples in order to automatically compute objective measures of neurological health. This document summarizes several contributions to expand upon this body of research. Mainly, there is still a large gap between the theoretical power of computational language models and their actual use in clinical applications. One of the largest concerns is the limited and inconsistent reliability of speech and language features used in models for assessing specific aspects of mental health; numerous methods may exist to measure the same or similar constructs and lead researchers to different conclusions in different studies. To address this, a novel measurement model based on a theoretical framework of speech production is used to motivate feature selection, while also performing a smoothing operation on features across several domains of interest. Then, these composite features are used to perform a much wider range of analyses than is typical of previous studies, looking at everything from diagnosis to functional competency assessments. Lastly, potential improvements to address practical implementation challenges associated with the use of speech and language technology in a real-world environment are investigated. The goal of this work is to demonstrate the ability of speech and language technology to aid clinical practitioners toward improvements in quality of life outcomes for their patients.
ContributorsVoleti, Rohit Nihar Uttam (Author) / Berisha, Visar (Thesis advisor) / Liss, Julie M (Thesis advisor) / Turaga, Pavan (Committee member) / Spanias, Andreas (Committee member) / Arizona State University (Publisher)
Created2022
Description
Parkinson’s Disease is one of the most complicated and abundantneurodegenerative diseases in the world. Previous analysis of Parkinson’s disease has
identified both speech and gait deficits throughout progression of the disease. There has
been minimal research looking into the correlation between both the speech and gait
deficits in those diagnosed with Parkinson’s. There is high indication that there is a
correlation between the two given the similar pathology and origins of both deficits. This
exploratory study aims to establish correlation between both the gait and speech deficits
in those diagnosed with Parkinson’s disease. Using previously identified motor and
speech measurements and tasks, I conducted a correlational study of individuals with
Parkinson’s disease at baseline. There were correlations between multiple speech and gait
variability outcomes. The expected correlations ranged from average harmonics-to-noise
ratio values against anticipatory postural adjustments-lateral peak distance to average
shimmer values against anticipatory postural adjustments-lateral peak distance. There
were also unexpected outcomes that ranged from F2 variability against the average
number of steps in a turn to intensity variability against step duration variability. I also
analyzed the speech changes over 1 year as a secondary outcome of the study. Finally, I
found that averages and variabilities increased over 1 year regarding speech primary
outcomes. This study serves as a basis for further treatment that may be able to
simultaneously treat both speech and gait deficits in those diagnosed with Parkinson’s.
The exploratory study also indicates multiple targets for further investigation to better
understand cohesive and compensatory mechanisms.
ContributorsBelnavis, Alexander Salvador (Author) / Peterson, Daniel (Thesis advisor) / Daliri, Ayoub (Committee member) / Berisha, Visar (Committee member) / Arizona State University (Publisher)
Created2022
Description
In the era of big data, more and more decisions and recommendations are being made by machine learning (ML) systems and algorithms. Despite their many successes, there have been notable deficiencies in the robustness, rigor, and reliability of these ML systems, which have had detrimental societal impacts. In the next generation of ML, these significant challenges must be addressed through careful algorithmic design, and it is crucial that practitioners and meta-algorithms have the necessary tools to construct ML models that align with human values and interests. In an effort to help address these problems, this dissertation studies a tunable loss function called α-loss for the ML setting of classification. The alpha-loss is a hyperparameterized loss function originating from information theory that continuously interpolates between the exponential (alpha = 1/2), log (alpha = 1), and 0-1 (alpha = infinity) losses, hence providing a holistic perspective of several classical loss functions in ML. Furthermore, the alpha-loss exhibits unique operating characteristics depending on the value (and different regimes) of alpha; notably, for alpha > 1, alpha-loss robustly trains models when noisy training data is present. Thus, the alpha-loss can provide robustness to ML systems for classification tasks, and this has bearing in many applications, e.g., social media, finance, academia, and medicine; indeed, results are presented where alpha-loss produces more robust logistic regression models for COVID-19 survey data with gains over state of the art algorithmic approaches.
ContributorsSypherd, Tyler (Author) / Sankar, Lalitha (Thesis advisor) / Berisha, Visar (Committee member) / Dasarathy, Gautam (Committee member) / Kosut, Oliver (Committee member) / Arizona State University (Publisher)
Created2022
Description
Three models have been created to visualize and characterize the voltage response of a standing wave accelerating cavity system. These models are generalized to fit any cavity with known values of the quality factor, coupling factor, and resonant frequency but were applied to the Arizona State Universities Compact X-ray Free Electron Laser. To model these systems efficiently, baseband I and Q measurements were used to eliminate the modeling of high frequencies. The three models discussed in this paper include a single standing wave cavity, two cavities coupled through a 3dB quadrature hybrid, and a pulse compression system. The second model on two coupled cavities will demonstrate how detuning will impact two cavities with the same RF source split through a hybrid. The pulse compression model will be used to demonstrate the impact of feeding pulse compression into a standing wave cavity. The pulse compressor will demonstrate more than a 50\% increase of the voltage inside the cavity.
ContributorsFalconer, Jasmin (Author) / Graves, William (Thesis director) / Kitchen, Jennifer (Committee member) / Barrett, The Honors College (Contributor) / Electrical Engineering Program (Contributor)
Created2023-05
Description新世纪以来中国电影的产业化改革与探索愈发呈现良好的态势，国产院线电影也在实践中努力赢得观众和票房市场。其中类型喜剧电影，最符合商业电影规律、最顺应影视市场需求、最能获得票房收益而备受影视创投机构、制作公司青睐。本论文研究对象聚焦类型喜剧电影，通过“欢声笑语里的财富”现象，探究类型喜剧电影内部本体构成要素与外部客观促成要素的关联；以通过分析自变量与因变量因素对中国电影票房之类型喜剧影响因素进行实证研究，为影视创投和影视制作总结并提供可靠建议。
本论文整体结构包括：第一部分为导论，包括研究背景、目的意义，相关文献综述与文献评述和论文创新性。第二部分聚焦类型喜剧本身，从电影学范畴的电影本体出发，探究“笑”的心理、社会与文化内涵，并分析将“笑”对经济领域的延伸。第三部分以影视投资、票房为依托，从现象和数据中探寻影响类型喜剧电影的因素，为展开中国电影票房之类型喜剧影响因素实证研究做好理论的铺垫。第四与第五部分则基于上述理论进行实证检验，选用2013-2020年电影样本，采用多元线性回归模型研究喜剧类型对票房的吸引力，以及不同种类型喜剧对电影票房的提振效果作用差异。研究发现喜剧电影对电影票房有显著的提振作用；以及研究电影的外部影响因素（续集效应）对电影票房的作用。发现续集电影有更好的票房表现，续集效应的票房提升作用在喜剧电影中表现的更加明显。
本论文研究成果最终将回归到“欢声笑语里的财富”本身；即“类型复合喜剧”对促进电影与金融产业的互动关联、实现更加可持续化发展，以及进而推动经济及文化业的发展。
ContributorsLiu, Yongqian (Author) / Shen, Wei (Thesis advisor) / Zhu, Ning (Thesis advisor) / Dong, Xiaodan (Committee member) / Arizona State University (Publisher)
Created2022