Regular Expressions and Transducers over Alphabet-invariant and User-defined Labels

We are interested in regular expressions and transducers that represent word relations in an alphabet-invariant way---for example, the set of all word pairs u,v where v is a prefix of u independently of what the alphabet is. Current software systems of formal language objects do not have a mechanism to define such objects. We define transducers in which transition labels involve what we call set specifications, some of which are alphabet invariant. In fact, we give a more broad definition of automata-type objects, called labelled graphs, where each transition label can be any string, as long as that string represents a subset of a certain monoid. Then, the behaviour of the labelled graph is a subset of that monoid. We do the same for regular expressions. We obtain extensions of a few classic algorithmic constructions on ordinary regular expressions and transducers at the broad level of labelled graphs and in such a way that the computational efficiency of the extended constructions is not sacrificed. For regular expressions with set specs we obtain the corresponding partial derivative automata. For transducers with set specs we obtain further algorithms that can be applied to questions about independent regular languages, in particular the witness version of the independent property satisfaction question

New experimental and computational methods for ultrasound brain tomography

Fast, portable, and affordable neuroimaging is currently unavailable in clinical practice, hindering prevention and treatment of pathologies such as stroke, a leading cause of death and disability worldwide. Full-waveform inversion (FWI), an ultrasound-based tomographic technique, has been recently proposed as a solution to this problem, but is yet to be successfully applied experimentally. Two fundamental barriers hinder its experimental application: a lack of numerical models that accurately replicate experimental measurements, and of domain-specific software that implements FWI algorithms efficiently. Here, I address both problems, opening the door to universally available neuroimaging. Addressing the first barrier entails finding numerical models that can explain the behaviour of the acquisition system: the transmission and reception response of the transducers, and their spatial location and orientation. As I demonstrate here, existing position-estimation methods fail when the surface of the transducers is bigger than the wavelength, a prerequisite for imaging through the skull, while available response-estimation techniques cannot achieve the precision required by full-wave methods. Therefore, I present spatial response identification, a new algorithm for transducer calibration and modelling, and show how it can be used to explain experimental devices with higher accuracy than existing methods. Additionally, I present experimental reconstructions of a tissue-mimicking phantom, achieving improved imaging quality with respect to standard calibration techniques. The second barrier stems from the fact that FWI is mathematically challenging and orders of magnitude more computationally expensive than conventional ultrasound imaging, while there is an absence of open codes, slowing the pace of research and hindering reproducibility. Therefore, I introduce Stride, an open-source Python library that combines high-level interfaces with automatically generated, high-performance solvers and scalable parallelisation. Here, I demonstrate that Stride can achieve state-of-the-art modelling accuracy and how it can be used to image in 2D and 3D, scaling from a local workstation to a high-performance cluster.Open Acces

Assessment of Factors Contributing to Refrigerator Cycling Losses

Thermal mass effects, refrigerant dynamics, and interchanger transients are three factors affecting the transient and cycling performance of all refrigeration and air conditioning equipment. The effects of refrigerant dynamics, including refrigerant/oil solubility, off-cycle migration, and charge redistribution, were found to be the most important. These effects are quantified for a refrigerator instrumented with immersion thermocouples, pressure transducers, and microphones. The analytical methods, however, are applicable to other types of refrigeration and air conditioning systems, including those with capillary tube/suction line heat exchangers.Air Conditioning and Refrigeration Center Project 3

Symbolic Model Learning: New Algorithms and Applications

In this thesis, we study algorithms which can be used to extract, or learn, formal mathematical models from software systems and then using these models to test whether the given software systems satisfy certain security properties such as robustness against code injection attacks. Specifically, we focus on studying learning algorithms for automata and transducers and the symbolic extensions of these models, namely symbolic finite automata (SFAs). In a high level, this thesis contributes the following results: 1. In the first part of the thesis, we present a unified treatment of many common variations of the seminal L* algorithm for learning deterministic finite automata (DFAs) as a congruence learning algorithm for the underlying Nerode congruence which forms the basis of automata theory. Under this formulation the basic data structures used by different variations are unified as different ways to implement the Nerode congruence using queries. 2. Next, building on the new formulation of L*-style algorithms we proceed to develop new algorithms for learning transducer models. Firstly, we present the first algorithm for learning deterministic partial transducers. Furthermore, we extend my algorithm into non-deterministic models by introducing a novel, generalized congruence relation over string transformations which is able to capture a subclass of string transformations with regular lookahead. We demonstrate that this class is able to capture many practical string transformation from the domain of string sanitizers in Web applications. 3. Classical learning algorithms for automata and transducers operate over finite alphabets and have a query complexity that scales linearly with the size of the alphabet. However, in practice, this dependence on the alphabet size hinders the performance of the algorithms. To address this issue, we develop the MAT* algorithm for learning symbolic finite state automata (SFAs) which operate over infinite alphabets. In practice, the MAT* learning algorithm allow us to plug custom transition learning algorithms which will efficiently infer the predicates in the transitions of the SFA without querying the whole alphabet set. 4. Finally, we use our learning algorithm toolbox as the basis for the development of a set of black-box testing algorithms. More specifically, we present Grammar Oriented Filter Auditing (GOFA), a novel technique which allows one to utilize my learning algorithms to evaluate the robustness of a string sanitizer or filter against a set of attack strings given as a context-free grammar. Furthermore, because such grammars are many times unavailable, we developed sfadiff a differential testing technique based on symbolic automata learning which can be used in order to perform differential testing of two different parser implementations using SFA learning algorithms and we demonstrate how our algorithm can be used to develop program fingerprints. We evaluate our algorithms against state-of-the-art Web Application Firewalls and discover over 15 previously unknown vulnerabilities which result in evading the firewalls and performing code injection attacks in the backend Web application. Finally, we show how our learning algorithms can uncover vulnerabilities which are missed by other black-box methods such as fuzzing and grammar-based testing

Ion Based Pressure Sensor for Pulse Detonation Engines

A high speed, durable, ion probe based pressure sensor is being investigated for use in pulse detonation engines. Traditional pressure sensors are ill suited for the high temperature and vibratory environment encountered in such engines. An alternative transient pressure sensing method is investigated for pressures behind a hydrocarbon flame. These flames generate ions that are quenched by collisions as a function of pressure. An experiment was devised to correlate the ion decay rate with the pressure using an ion probe well suited for the flow. A correlation has been established showing the ion decay rate is a function of pressure. Additional investigation is required even though the ion probe remains a viable alternative method for measuring pressure

DEVELOPMENT OF A SIMPLIFIED, MASS PRODUCIBLE HYBRIDIZED AMBIENT, LOW FREQUENCY, LOW INTENSITY VIBRATION ENERGY SCAVENGER (HALF-LIVES)

Scavenging energy from environmental sources is an active area of research to enable remote sensing and microsystems applications. Furthermore, as energy demands soar, there is a significant need to explore new sources and curb waste. Vibration energy scavenging is one environmental source for remote applications and a candidate for recouping energy wasted by mechanical sources that can be harnessed to monitor and optimize operation of critical infrastructure (e.g. Smart Grid). Current vibration scavengers are limited by volume and ancillary requirements for operation such as control circuitry overhead and battery sources. This dissertation, for the first time, reports a mass producible hybrid energy scavenger system that employs both piezoelectric and electrostatic transduction on a common MEMS device. The piezoelectric component provides an inherent feedback signal and pre-charge source that enables electrostatic scavenging operation while the electrostatic device provides the proof mass that enables low frequency operation. The piezoelectric beam forms the spring of the resonant mass-spring transducer for converting vibration excitation into an AC electrical output. A serially poled, composite shim, piezoelectric bimorph produces the highest output rectified voltage of over 3.3V and power output of 145uW using ¼ g vibration acceleration at 120Hz. Considering solely the volume of the piezoelectric beam and tungsten proof mass, the volume is 0.054cm3, resulting in a power density of 2.68mW/cm3. Incorporation of a simple parallel plate structure that provides the proof mass for low frequency resonant operation in addition to cogeneration via electrostatic energy scavenging provides a 19.82 to 35.29 percent increase in voltage beyond the piezoelectric generated DC rails. This corresponds to approximately 2.1nW additional power from the electrostatic scavenger component and demonstrates the first instance of hybrid energy scavenging using both piezoelectric and synchronous electrostatic transduction. Furthermore, it provides a complete system architecture and development platform for additional enhancements that will enable in excess of 100uW additional power from the electrostatic scavenger