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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
Modeling dynamic systems is an interesting problem in Knowledge Representation (KR) due to their usefulness in reasoning about real-world environments. In order to effectively do this, a number of different formalisms have been considered ranging from low-level languages, such as Answer Set Programming (ASP), to high-level action languages, such as C+ and BC. These languages show a lot of promise over many traditional approaches as they allow a developer to automate many tasks which require reasoning within dynamic environments in a succinct and elaboration tolerant manner. However, despite their strengths, they are still insufficient for modeling many systems, especially those of non-trivial scale or that require the ability to cope with exceptions which occur during execution, such as unexpected events or unintended consequences to actions which have been performed. In order to address these challenges, a theoretical framework is created which focuses on improving the feasibility of applying KR techniques to such problems. The framework is centered on the action language BC+, which integrates many of the strengths of existing KR formalisms, and provides the ability to perform efficient reasoning in an incremental fashion while handling exceptions which occur during execution. The result is a developer friendly formalism suitable for performing reasoning in an online environment. Finally, the newly enhanced Cplus2ASP 2 is introduced, which provides a number of improvements over the original version. These improvements include implementing BC+ among several additional languages, providing enhanced developer support, and exhibiting a significant performance increase over its predecessors and similar systems.
ContributorsBabb, Joseph (Author) / Lee, Joohyung (Thesis advisor) / Lee, Yann-Hang (Committee member) / Baral, Chitta (Committee member) / Arizona State University (Publisher)
Created2014
Description
Action language C+ is a formalism for describing properties of actions, which is based on nonmonotonic causal logic. The definite fragment of C+ is implemented in the Causal Calculator (CCalc), which is based on the reduction of nonmonotonic causal logic to propositional logic. This thesis describes the language of CCalc in terms of answer set programming (ASP), based on the translation of nonmonotonic causal logic to formulas under the stable model semantics. I designed a standard library which describes the constructs of the input language of CCalc in terms of ASP, allowing a simple modular method to represent CCalc input programs in the language of ASP. Using the combination of system F2LP and answer set solvers, this method achieves functionality close to that of CCalc while taking advantage of answer set solvers to yield efficient computation that is orders of magnitude faster than CCalc for many benchmark examples. In support of this, I created an automated translation system Cplus2ASP that implements the translation and encoding method and automatically invokes the necessary software to solve the translated input programs.
ContributorsCasolary, Michael (Author) / Lee, Joohyung (Thesis advisor) / Ahn, Gail-Joon (Committee member) / Baral, Chitta (Committee member) / Arizona State University (Publisher)
Created2011
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
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
Answer Set Programming (ASP) is one of the main formalisms in Knowledge Representation (KR) that is being widely applied in a large number of applications. While ASP is effective on Boolean decision problems, it has difficulty in expressing quantitative uncertainty and probability in a natural way.
Logic Programs under the answer set semantics and Markov Logic Network (LPMLN) is a recent extension of answer set programs to overcome the limitation of the deterministic nature of ASP by adopting the log-linear weight scheme of Markov Logic. This thesis investigates the relationships between LPMLN and two other extensions of ASP: weak constraints to express a quantitative preference among answer sets, and P-log to incorporate probabilistic uncertainty. The studied relationships show how different extensions of answer set programs are related to each other, and how they are related to formalisms in Statistical Relational Learning, such as Problog and MLN, which have shown to be closely related to LPMLN. The studied relationships compare the properties of the involved languages and provide ways to compute one language using an implementation of another language.
This thesis first presents a translation of LPMLN into programs with weak constraints. The translation allows for computing the most probable stable models (i.e., MAP estimates) or probability distribution in LPMLN programs using standard ASP solvers so that the well-developed techniques in ASP can be utilized. This result can be extended to other formalisms, such as Markov Logic, ProbLog, and Pearl’s Causal Models, that are shown to be translatable into LPMLN.
This thesis also presents a translation of P-log into LPMLN. The translation tells how probabilistic nonmonotonicity (the ability of the reasoner to change his probabilistic model as a result of new information) of P-log can be represented in LPMLN, which yields a way to compute P-log using standard ASP solvers or MLN solvers.
Logic Programs under the answer set semantics and Markov Logic Network (LPMLN) is a recent extension of answer set programs to overcome the limitation of the deterministic nature of ASP by adopting the log-linear weight scheme of Markov Logic. This thesis investigates the relationships between LPMLN and two other extensions of ASP: weak constraints to express a quantitative preference among answer sets, and P-log to incorporate probabilistic uncertainty. The studied relationships show how different extensions of answer set programs are related to each other, and how they are related to formalisms in Statistical Relational Learning, such as Problog and MLN, which have shown to be closely related to LPMLN. The studied relationships compare the properties of the involved languages and provide ways to compute one language using an implementation of another language.
This thesis first presents a translation of LPMLN into programs with weak constraints. The translation allows for computing the most probable stable models (i.e., MAP estimates) or probability distribution in LPMLN programs using standard ASP solvers so that the well-developed techniques in ASP can be utilized. This result can be extended to other formalisms, such as Markov Logic, ProbLog, and Pearl’s Causal Models, that are shown to be translatable into LPMLN.
This thesis also presents a translation of P-log into LPMLN. The translation tells how probabilistic nonmonotonicity (the ability of the reasoner to change his probabilistic model as a result of new information) of P-log can be represented in LPMLN, which yields a way to compute P-log using standard ASP solvers or MLN solvers.
ContributorsYang, Zhun (Author) / Lee, Joohyung (Thesis advisor) / Baral, Chitta (Committee member) / Li, Baoxin (Committee member) / Arizona State University (Publisher)
Created2017
Description
Natural Language Processing is a subject that combines computer science and linguistics, aiming to provide computers with the ability to understand natural language and to develop a more intuitive human-computer interaction. The research community has developed ways to translate natural language to mathematical formalisms. It has not yet been shown, however, how to automatically translate different kinds of knowledge in English to distinct formal languages. Most of the recent work presents the problem that the translation method aims to a specific formal language or is hard to generalize. In this research, I take a first step to overcome this difficulty and present two algorithms which take as input two lambda-calculus expressions G and H and compute a lambda-calculus expression F. The expression F returned by the first algorithm satisfies F@G=H and, in the case of the second algorithm, we obtain G@F=H. The lambda expressions represent the meanings of words and sentences. For each formal language that one desires to use with the algorithms, the language must be defined in terms of lambda calculus. Also, some additional concepts must be included. After doing this, given a sentence, its representation and knowing the representation of several words in the sentence, the algorithms can be used to obtain the representation of the other words in that sentence. In this work, I define two languages and show examples of their use with the algorithms. The algorithms are illustrated along with soundness and completeness proofs, the latter with respect to typed lambda-calculus formulas up to the second order. These algorithms are a core part of a natural language semantics system that translates sentences from English to formulas in different formal languages.
ContributorsAlvarez Gonzalez, Marcos (Author) / Baral, Chitta (Thesis advisor) / Lee, Joohyung (Committee member) / Ye, Jieping (Committee member) / Arizona State University (Publisher)
Created2010