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- Genre: Masters Thesis
- Creators: Arizona State University
- Creators: Chakraborty, Bijeta
- Creators: Peterson, Cory
- Member of: ASU Electronic Theses and Dissertations
Description
Efficiency of components is an ever increasing area of importance to portable applications, where a finite battery means finite operating time. Higher efficiency devices need to be designed that don't compromise on the performance that the consumer has come to expect. Class D amplifiers deliver on the goal of increased efficiency, but at the cost of distortion. Class AB amplifiers have low efficiency, but high linearity. By modulating the supply voltage of a Class AB amplifier to make a Class H amplifier, the efficiency can increase while still maintaining the Class AB level of linearity. A 92dB Power Supply Rejection Ratio (PSRR) Class AB amplifier and a Class H amplifier were designed in a 0.24um process for portable audio applications. Using a multiphase buck converter increased the efficiency of the Class H amplifier while still maintaining a fast response time to respond to audio frequencies. The Class H amplifier had an efficiency above the Class AB amplifier by 5-7% from 5-30mW of output power without affecting the total harmonic distortion (THD) at the design specifications. The Class H amplifier design met all design specifications and showed performance comparable to the designed Class AB amplifier across 1kHz-20kHz and 0.01mW-30mW. The Class H design was able to output 30mW into 16Ohms without any increase in THD. This design shows that Class H amplifiers merit more research into their potential for increasing efficiency of audio amplifiers and that even simple designs can give significant increases in efficiency without compromising linearity.
ContributorsPeterson, Cory (Author) / Bakkaloglu, Bertan (Thesis advisor) / Barnaby, Hugh (Committee member) / Kiaei, Sayfe (Committee member) / Arizona State University (Publisher)
Created2013
Description
Class D Amplifiers are widely used in portable systems such as mobile phones to achieve high efficiency. The demands of portable electronics for low power consumption to extend battery life and reduce heat dissipation mandate efficient, high-performance audio amplifiers. The high efficiency of Class D amplifiers (CDAs) makes them particularly attractive for portable applications. The Digital class D amplifier is an interesting solution to increase the efficiency of embedded systems. However, this solution is not good enough in terms of PWM stage linearity and power supply rejection. An efficient control is needed to correct the error sources in order to get a high fidelity sound quality in the whole audio range of frequencies. A fundamental analysis on various error sources due to non idealities in the power stage have been discussed here with key focus on Power supply perturbations driving the Power stage of a Class D Audio Amplifier. Two types of closed loop Digital Class D architecture for PSRR improvement have been proposed and modeled. Double sided uniform sampling modulation has been used. One of the architecture uses feedback around the power stage and the second architecture uses feedback into digital domain. Simulation & experimental results confirm that the closed loop PSRR & PS-IMD improve by around 30-40 dB and 25 dB respectively.
ContributorsChakraborty, Bijeta (Author) / Bakkaloglu, Bertan (Thesis advisor) / Garrity, Douglas (Committee member) / Ozev, Sule (Committee member) / Arizona State University (Publisher)
Created2012
Description
The Semantic Web contains large amounts of related information in the form of knowledge graphs such as DBpedia. These knowledge graphs are typically enormous and are not easily accessible for users as they need specialized knowledge in query languages (such as SPARQL) as well as deep familiarity of the ontologies used by these knowledge graphs. So, to make these knowledge graphs more accessible (even for non- experts) several question answering (QA) systems have been developed over the last decade. Due to the complexity of the task, several approaches have been undertaken that include techniques from natural language processing (NLP), information retrieval (IR), machine learning (ML) and the Semantic Web (SW). At a higher level, most question answering systems approach the question answering task as a conversion from the natural language question to its corresponding SPARQL query. These systems then utilize the query to retrieve the desired entities or literals. One approach to solve this problem, that is used by most systems today, is to apply deep syntactic and semantic analysis on the input question to derive the SPARQL query. This has resulted in the evolution of natural language processing pipelines that have common characteristics such as answer type detection, segmentation, phrase matching, part-of-speech-tagging, named entity recognition, named entity disambiguation, syntactic or dependency parsing, semantic role labeling, etc.
This has lead to NLP pipeline architectures that integrate components that solve a specific aspect of the problem and pass on the results to subsequent components for further processing eg: DBpedia Spotlight for named entity recognition, RelMatch for relational mapping, etc. A major drawback in this approach is error propagation that is a common problem in NLP. This can occur due to mistakes early on in the pipeline that can adversely affect successive steps further down the pipeline. Another approach is to use query templates either manually generated or extracted from existing benchmark datasets such as Question Answering over Linked Data (QALD) to generate the SPARQL queries that is basically a set of predefined queries with various slots that need to be filled. This approach potentially shifts the question answering problem into a classification task where the system needs to match the input question to the appropriate template (class label).
This thesis proposes a neural network approach to automatically learn and classify natural language questions into its corresponding template using recursive neural networks. An obvious advantage of using neural networks is the elimination for the need of laborious feature engineering that can be cumbersome and error prone. The input question would be encoded into a vector representation. The model will be trained and evaluated on the LC-QuAD Dataset (Large-scale Complex Question Answering Dataset). The dataset was created explicitly for machine learning based QA approaches for learning complex SPARQL queries. The dataset consists of 5000 questions along with their corresponding SPARQL queries over the DBpedia dataset spanning 5042 entities and 615 predicates. These queries were annotated based on 38 unique templates that the model will attempt to classify. The resulting model will be evaluated against both the LC-QuAD dataset and the Question Answering Over Linked Data (QALD-7) dataset.
The recursive neural network achieves template classification accuracy of 0.828 on the LC-QuAD dataset and an accuracy of 0.618 on the QALD-7 dataset. When the top-2 most likely templates were considered the model achieves an accuracy of 0.945 on the LC-QuAD dataset and 0.786 on the QALD-7 dataset.
After slot filling, the overall system achieves a macro F-score 0.419 on the LC- QuAD dataset and a macro F-score of 0.417 on the QALD-7 dataset.
This has lead to NLP pipeline architectures that integrate components that solve a specific aspect of the problem and pass on the results to subsequent components for further processing eg: DBpedia Spotlight for named entity recognition, RelMatch for relational mapping, etc. A major drawback in this approach is error propagation that is a common problem in NLP. This can occur due to mistakes early on in the pipeline that can adversely affect successive steps further down the pipeline. Another approach is to use query templates either manually generated or extracted from existing benchmark datasets such as Question Answering over Linked Data (QALD) to generate the SPARQL queries that is basically a set of predefined queries with various slots that need to be filled. This approach potentially shifts the question answering problem into a classification task where the system needs to match the input question to the appropriate template (class label).
This thesis proposes a neural network approach to automatically learn and classify natural language questions into its corresponding template using recursive neural networks. An obvious advantage of using neural networks is the elimination for the need of laborious feature engineering that can be cumbersome and error prone. The input question would be encoded into a vector representation. The model will be trained and evaluated on the LC-QuAD Dataset (Large-scale Complex Question Answering Dataset). The dataset was created explicitly for machine learning based QA approaches for learning complex SPARQL queries. The dataset consists of 5000 questions along with their corresponding SPARQL queries over the DBpedia dataset spanning 5042 entities and 615 predicates. These queries were annotated based on 38 unique templates that the model will attempt to classify. The resulting model will be evaluated against both the LC-QuAD dataset and the Question Answering Over Linked Data (QALD-7) dataset.
The recursive neural network achieves template classification accuracy of 0.828 on the LC-QuAD dataset and an accuracy of 0.618 on the QALD-7 dataset. When the top-2 most likely templates were considered the model achieves an accuracy of 0.945 on the LC-QuAD dataset and 0.786 on the QALD-7 dataset.
After slot filling, the overall system achieves a macro F-score 0.419 on the LC- QuAD dataset and a macro F-score of 0.417 on the QALD-7 dataset.
ContributorsAthreya, Ram G (Author) / Bansal, Srividya (Thesis advisor) / Usbeck, Ricardo (Committee member) / Gary, Kevin (Committee member) / Arizona State University (Publisher)
Created2018
Description
In this thesis, a digital input class D audio amplifier system which has the ability
to reject the power supply noise and nonlinearly of the output stage is presented. The main digital class D feed-forward path is using the fully-digital sigma-delta PWM open loop topology. Feedback loop is used to suppress the power supply noise and harmonic distortions. The design is using global foundry 0.18um technology.
Based on simulation, the power supply rejection at 200Hz is about -49dB with
81dB dynamic range and -70dB THD+N. The full scale output power can reach as high as 27mW and still keep minimum -68dB THD+N. The system efficiency at full scale is about 82%.
to reject the power supply noise and nonlinearly of the output stage is presented. The main digital class D feed-forward path is using the fully-digital sigma-delta PWM open loop topology. Feedback loop is used to suppress the power supply noise and harmonic distortions. The design is using global foundry 0.18um technology.
Based on simulation, the power supply rejection at 200Hz is about -49dB with
81dB dynamic range and -70dB THD+N. The full scale output power can reach as high as 27mW and still keep minimum -68dB THD+N. The system efficiency at full scale is about 82%.
ContributorsBai, Jing (Author) / Bakkaloglu, Bertan (Thesis advisor) / Arizona State University (Publisher)
Created2015
Description
Question answering is a challenging problem and a long term goal of Artificial Intelligence. There are many approaches proposed to solve this problem, including end to end machine learning systems, Information Retrieval based approaches and Textual Entailment. Despite being popular, these methods find difficulty in solving problems that require multi level reasoning and combining independent pieces of knowledge, for example, a question like "What adaptation is necessary in intertidal ecosystems but not in reef ecosystems?'', requires the system to consider qualities, behaviour or features of an organism living in an intertidal ecosystem and compare with that of an organism in a reef ecosystem to find the answer. The proposed solution is to solve a genre of questions, which is questions based on "Adaptation, Variation and Behavior in Organisms", where there are various different independent sets of knowledge required for answering questions along with reasoning. This method is implemented using Answer Set Programming and Natural Language Inference (which is based on machine learning ) for finding which of the given options is more probable to be the answer by matching it with the knowledge base. To evaluate this approach, a dataset of questions and a knowledge base in the domain of "Adaptation, Variation and Behavior in Organisms" is created.
ContributorsBatni, Vaishnavi (Author) / Baral, Chitta (Thesis advisor) / Anwar, Saadat (Committee member) / Yang, Yezhou (Committee member) / Arizona State University (Publisher)
Created2019
Description
One of the measures to determine the intelligence of a system is through Question Answering, as it requires a system to comprehend a question and reason using its knowledge base to accurately answer it. Qualitative word problems are an important subset of such problems, as they require a system to recognize and reason with qualitative knowledge expressed in natural language. Traditional approaches in this domain include multiple modules to parse a given problem and to perform the required reasoning. Recent approaches involve using large pre-trained Language models like the Bidirection Encoder Representations from Transformers for downstream question answering tasks through supervision. These approaches however either suffer from errors between multiple modules, or are not interpretable with respect to the reasoning process employed. The proposed solution in this work aims to overcome these drawbacks through a single end-to-end trainable model that performs both the required parsing and reasoning. The parsing is achieved through an attention mechanism, whereas the reasoning is performed in vector space using soft logic operations. The model also enforces constraints in the form of auxiliary loss terms to increase the interpretability of the underlying reasoning process. The work achieves state of the art accuracy on the QuaRel dataset and matches that of the QuaRTz dataset with additional interpretability.
ContributorsNarayana, Sanjay (Author) / Baral, Chitta (Thesis advisor) / Mitra, Arindam (Committee member) / Anwar, Saadat (Committee member) / Arizona State University (Publisher)
Created2020
Description
Systematic Reviews (SRs) aim to synthesize the totality of evidence for clinical practice and are important in making clinical practice guidelines and health policy decisions. However, conducting SRs manually is a laborious and time-consuming process. This challenge is growing due to the increase in the number of databases to search and the papers being published. Hence, the automation of SRs is an essential task. The goal of this thesis work is to develop Natural Language Processing (NLP)-based classifiers to automate the title and abstract-based screening for clinical SRs based on inclusion/exclusion criteria. In clinical SRs, a high-sensitivity system is a key requirement. Most existing methods for SRs use binary classification systems trained on labeled data to predict inclusion/exclusion. While previous studies have shown that NLP-based classification methods can automate title and abstract-based screening for SRs, methods for achieving high-sensitivity have not been empirically studied. In addition, the training strategy for binary classification has several limitations: (1) it ignores the inclusion/exclusion criteria, (2) lacks generalization ability, (3) suffers from low resource data, and (4) fails to achieve reasonable precision at high-sensitivity levels. This thesis work presents contributions to several aspects of the clinical systematic review domain. First, it presents an empirical study of NLP-based supervised text classification and high-sensitivity methods on datasets developed from six different SRs in the clinical domain. Second, this thesis work provides a novel approach to view SR as a Question Answering (QA) problem in order to overcome the limitations of the binary classification training strategy; and propose a more general abstract screening model for different SRs. Finally, this work provides a new QA-based dataset for six different SRs which is made available to the community.
ContributorsParmar, Mihir Prafullsinh (Author) / Baral, Chitta (Thesis advisor) / Devarakonda, Murthy (Thesis advisor) / Riaz, Irbaz B (Committee member) / Arizona State University (Publisher)
Created2021