Implementation of semi-autonomous vehicle for environmental feedback

In a world marked by war and terrorism, the Department of Defense has found an increasing need for the use of unmanned ground vehicles (UGV) to facilitate the safety of its soldiers in the battlefield. UGVs can be used for scouting and surveying enemy territory instead of placing soldiers at risk. In response to this matter, the objective of this report is to propose a design for a microcontroller based vehicle that can give the user feedback on the environment. We propose to implement a camera sensor on a vehicle that will be used to track and follow a marked line on the ground. Additional information including direction and speed of the vehicle will be recorded using magnetometer and accelerometer sensors, respectively. The direction information is needed to give the user an idea of the vehicle’s orientation. The speed information is needed to control the response of the motors based upon the type of ground topography (e.g. sloped surface vs. flat surface). The goal of the project is to implement the combination of these three sensors (camera, magnetometer, and accelerometer) for better control of the vehicle in its environment. The intended deliverable will be a small vehicle powered by a rechargeable battery and controlled by the ATMega16 microcontroller. The vehicle will also contain the accelerometer, magnetometer, and camera sensors

\mu to e in R-symmetric Supersymmetry

We demonstrate that mu/e slepton mixing is significantly more restricted than previously thought within the already remarkably flavor-safe R-symmetric supersymmetric standard model. We calculate bounds from mu to e gamma, mu to 3e and, most importantly, mu to e conversion. The process mu to e conversion is significantly more restrictive in R-symmetric models since this process can occur through operators that do not require a chirality-flip. We delineate the allowed parameter space, demonstrating that maximal mixing is rarely possible with weak scale superpartners, while O(0.1) mixing is permitted within most of the space. The best approach to find or rule out mu/e mixing in R-symmetric supersymmetric models is a multi-pronged attack looking at both mu to e conversion as well as mu to e gamma. The redundancy eliminates much of the parameter space where one process, but not both processes, contain amplitudes that accidentally destructively interfere. We briefly discuss implications for searches of slepton flavor violation at the LHC.Comment: 31 pages, 16 figures; Typos fixed, minor corrections to fig. 13, version published in PR

It is a Graviton! or maybe not

The discovery of Kaluza-Klein (KK) gravitons is a smoking gun of extra dimensions. Other scenarios, however, could give rise to spin-two resonances of a new strongly-coupled sector and act as impostors. In this paper we prove that a spin-two resonance does not couple to the Standard Model through dimension-four operators. We then show that the massive graviton and its impostor both couple to the Standard Model through the same dimension-five operators. Therefore the spin determination is identical. Nevertheless, we also show that one can use the ratio of branching ratios to photons and to jets for distinguishing between KK gravitons and their impostors. The capacity to distinguish between KK gravitons and impostors is a manifestation of the breakdown of the duality between AdS and strongly-coupled theories.Comment: 14 pages, 3 figures, 1 table. References added, typos correcte

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

Scenarios of Physics Beyond the Standard Model

xviii, 124 p. : ill. (some col.)This dissertation discusses three topics on scenarios beyond the Standard Model. Topic one is the effects from a fourth generation of quarks and leptons on electroweak baryogenesis in the early universe. The Standard Model is incapable of electroweak baryogenesis due to an insufficiently strong enough electroweak phase transition (EWPT) as well as insufficient CP violation. We show that the presence of heavy fourth generation fermions solves the first problem but requires additional bosons to be included to stabilize the electroweak vacuum. Introducing supersymmetric partners of the heavy fermions, we find that the EWPT can be made strong enough and new sources of CP violation are present. Topic two relates to the lepton avor problem in supersymmetry. In the Minimal Supersymmetric Standard Model (MSSM), the off-diagonal elements in the slepton mass matrix must be suppressed at the 10-3 level to avoid experimental bounds from lepton avor changing processes. This dissertation shows that an enlarged R-parity can alleviate the lepton avor problem. An analysis of all sensitive parameters was performed in the mass range below 1 TeV, and we find that slepton maximal mixing is possible without violating bounds from the lepton avor changing processes: μ [arrow right] eγ; μ [arrow right] e conversion, and μ [arrow right] 3e. Topic three is the collider phenomenology of quirky dark matter. In this model, quirks are particles that are gauged under the electroweak group, as well as a \dark" color SU (2) group. The hadronization scale of this color group is well below the quirk masses. As a result, the dark color strings never break. Quirk and anti-quirk pairs can be produced at the LHC. Once produced, they immediately form a bound state of high angular momentum. The quirk pair rapidly shed angular momentum by emitting soft radiation before they annihilate into observable signals. This dissertation presents the decay branching ratios of quirkonia where quirks obtain their masses through electroweak symmetry breaking. This dissertation includes previously published and unpublished co-authored material.Committee in charge: Dr. Davison Soper: Chair; Dr. Graham Kribs: Advisor; Dr. Ray Frey: Member; Dr. Michael Kellman: Outside Membe

Grammar Refinement for Grammar-Based Test Input Generation

Grammar-based fuzzing is an effective method of testing software that requires highly structured inputs. However, these fuzzers require a user-provided input grammar that often does not exist for niche and emerging domains.Grammar inference algorithms fill this gap by inferring a grammar using a set of training example inputs that satisfy a black-box oracle. However, existing grammar inference algorithms have shown to require on the order of hours to days to infer an input grammar from a handful of examples. We observe that some software systems such as AWS CloudFormation (CF) and Ansible take inputs that conform to a schema built on top of common formats like JSON or YAML.We introduce GramRefine, a grammar inference algorithm that refines the given base grammars into more precise input grammars. By using the base grammar as a starting point, we significantly reduce the time needed to infer a grammar and obviate the need for language-specific heuristics, such as matching parenthesis or specific delimiter usages.We conduct a comprehensive evaluation of GramRefine against the recent state-of-the-art black-box grammar inference algorithm TreeVada [4] on AWS CloudFormation templates, Ansible playbooks, and four sets of MLIR dialects, using a generic JSON grammar and generic MLIR grammar as the base grammars respectively. GramRefine is on average 654x faster and achieves 194x greater branch coverage than TreeVada. GramRefine's refined grammars creates qualitatively better examples than the base grammars---With more work to improve input validity (i.e oracle satisfaction), this could eventually translate to better coverage. GramRefine has the significant potential to improve input coverage and fault detection, while altering the burden to write a complex grammar by hand