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Understanding the noise in gravitational-wave detectors is central to detecting and interpreting gravitational-wave signals. Glitches are transient, non-Gaussian noise features that can have a range of environmental and instrumental origins. The Gravity Spy project uses a machine-learning algorithm to classify glitches based upon their time–frequency morphology. The resulting set of classified glitches can be used as input to detector-characterisation investigations of how to mitigate glitches, or data-analysis studies of how to ameliorate the impact of glitches. Here we present the results of the Gravity Spy analysis of data up to the end of the third observing run of advanced laser interferometric gravitational-wave observatory (LIGO). We classify 233981 glitches from LIGO Hanford and 379805 glitches from LIGO Livingston into morphological classes. We find that the distribution of glitches differs between the two LIGO sites. This highlights the potential need for studies of data quality to be individually tailored to each gravitational-wave observatory.

 

In September 2019, the fourth and final workshop on the Future of Mechatronics and Robotics Education (FoMRE) was held at a Lawrence Technological University in Southfield, MI. This workshop was organized by faculty at several universities with financial support from industry partners and the National Science Foundation. The purpose of the workshops was to create a cohesive effort among mechatronics and robotics courses, minors and degree programs. Mechatronics and Robotics Engineering (MRE) is an integration of mechanics, controls, electronics, and software, which provides a unique opportunity for engineering students to function on multidisciplinary teams. Due to its multidisciplinary nature, it attracts diverse and innovative students, and graduates better-prepared professional engineers. In this fast growing field, there is a great need to standardize educational material and make MRE education more widely available and easier to adopt. This can only be accomplished if the community comes together to speak with one clear voice about not only the benefits, but also the best ways to teach it. These efforts would also aid in establishing more of these degree programs and integrating minors or majors into existing computer science, mechanical engineering, or electrical engineering departments. The final workshop was attended by approximately 50 practitioners from industry and academia. Participants identified many practical skills required for students to succeed in an MRE curriculum and as practicing engineers after graduation. These skills were then organized into the following categories: professional, independent learning, controller design, numerical simulation and analysis, electronics, software development, and system design. For example, professional skills include technical reports, presentations, and documentation. Independent learning includes reading data sheets, performing internet searches, doing a literature review, and having a maker mindset. Numerical simulation skills include understanding data, presenting data graphically, solving and simulating in software such as MATLAB, Simulink and Excel. Controller design involves selecting a controller, tuning a controller, designing to meet specifications, and understanding when the results are good enough. Electronics skills include selecting sensors, interfacing sensors, interfacing actuators, creating printed circuit boards, wiring on a breadboard, soldering, installing drivers, using integrated circuits, and using microcontrollers. Software development of embedded systems includes agile program design, state machines, analyzing and evaluating code results, commenting code, troubleshooting, debugging, AI and machine learning. Finally, system design includes prototyping, creating CAD models, design for manufacturing, breaking a system down into subsystems, integrating and interfacing subcomponents, having a multidisciplinary perspective, robustness, evaluating tradeoffs, testing, validation, and verification, failure, effect, and mode analysis. A survey was prepared and sent out to the participants from all four workshops as well as other robotics faculty, researchers and industry personnel in order to elicit a broader community response. Because one of the biggest challenges in mechatronics and robotics education is the absence of standardized curricula, textbooks, platforms, syllabi, assignments, and learning outcomes, this was a vital part of the process to achieve some level of consensus. This paper presents an introduction to MRE education, related work on existing programs, methods, results of the practical skills survey, and then draws conclusions based upon these results. It aims to create the foundation for standardizing the development of student skills in mechatronics and robotics curricula across institutions, disciplines, majors and minors. The survey was completed by 94 participants and it was clear that there is a consensus that the primary skills students should have upon completion of MRE courses or a program is a broader multidisciplinary systems-level perspective, an ability to problem solve, and an ability to design a system to meet specifications. 

It is well-known that women and minorities are underrepresented in STEM fields. This is true of mechatronics and robotics engineering (MRE), despite targeted K-12 activities, such as the FIRST Robotics Competition, that aim to increase diversity in engineering. This paper is a first step in assessing the current status of women and underrepresented minorities (URM) as well as investigating solutions to increase diversity and support inclusion of these groups specifically in MRE. The paper examines challenges and potential solutions identified in The 4th Future of Mechatronics and Robotics Education and in an online survey of the MRE college instructor community. Survey participants reported on courses, programs, clubs, and outreach events at the college level. The sample size is small, but the data provide initial findings to inform further study. Qualitative text analysis was used with the survey data. Five themes emerged, ordered from most frequent to least: the instructor’s perspective, social context of MRE, specific attributes of MRE, pre-college interventions, and in-college interventions. The most promising new ideas are in curriculum reform to incorporate social context into engineering education and in expanding STEM outreach by colleges to elementary and middle schools. Existing programs should also be strengthened, including robotics competitions, NSF Research Experiences for Undergraduates, STEM summer camps, bridge programs, and affinity programs. Other important aspects include actively engaging parents, and working to be more inclusive of first-generation Americans and first-generation college students. The paper concludes with initial suggestions to increase diversity and inclusion in MRE and areas for further study. 

The evolution of Mechatronics and Robotics Engineering (MRE) has enabled numerous technological advancements since the early 20th century. Professionals in this field are reshaping the world by designing smart and autonomous systems aiming to improve human well-being. Recognizing the need for preparing highly-educated MRE professionals, many universities and colleges are adopting MRE as a distinct degree program. One of the cornerstones of MRE education is laboratory- and project-based learning to provide a hands-on and engaging experience for the students. To this end, numerous software and hardware platforms have been developed and utilized in MRE courses and laboratories. Commercial products can provide a rich hands-on experience for the students, but they can be cost-prohibitive. On the other hand, open-source platforms are low-cost alternatives to their commercial counterparts and are being increasingly used in industry. Developing open-source laboratory platforms will be a more feasible option for a wider range of institutions and would enable familiarizing the students with recent technological trends in industry and exposing them to the development details of a real-world system. However, adoption of open-source platforms in MRE courses can be lengthy and time consuming. Educators who wish to utilize such systems typically lack the expertise in all aspects of their implementation which can make them difficult to troubleshoot. Debugging open-source systems can also be challenging because most of the troubleshooting is done through forum discussions which appear to be very noisy and unfocused. The flip side of this chaotic nature of the open-source world is that there is a vast amount of information available, including tutorials, examples, and commentary and, with some focused searching, debugging and usage questions can often get answered. There is also a disconnect between the forum participants, typically computer scientists and hobbyists, and MRE educators and students. Finally, the available resources and documentation for utilizing open-source platforms in MRE education are insufficient and incomprehensive. Therefore, the main goal of this paper is to increase awareness and familiarity with the use of open-source software and hardware packages in MRE education and practice towards accelerating their adoption. To this end, open-source software packages such as Python, GNU Octave, OpenFOAM, Java, Modelica, Gazebo, SPICE, Scilab, and Gnuplot, which have the potential to be useful in the modeling and analysis of MRE systems are introduced. Furthermore, low-cost and powerful open-source hardware packages such as Arduino, Raspberry Pi, and BeagleBone which can be used as the main processing unit for data acquisition and control implementation in a wide range of MRE systems are reviewed and their limitations and potentials are investigated. This paper provides a valuable resource for MRE students and faculty who would like to utilize open-source hardware and software platforms in their education and research. 

We present Fermi Gamma-ray Burst Monitor (Fermi-GBM) and Swift Burst Alert Telescope (Swift-BAT) searches for gamma-ray/X-ray counterparts to gravitational-wave (GW) candidate events identified during the third observing run of the Advanced LIGO and Advanced Virgo detectors. Using Fermi-GBM onboard triggers and subthreshold gamma-ray burst (GRB) candidates found in the Fermi-GBM ground analyses, the Targeted Search and the Untargeted Search, we investigate whether there are any coincident GRBs associated with the GWs. We also search the Swift-BAT rate data around the GW times to determine whether a GRB counterpart is present. No counterparts are found. Using both the Fermi-GBM Targeted Search and the Swift-BAT search, we calculate flux upper limits and present joint upper limits on the gamma-ray luminosity of each GW. Given these limits, we constrain theoretical models for the emission of gamma rays from binary black hole mergers.

 

Gravitational waves provide a unique tool for observational astronomy. While the first LIGO–Virgo catalogue of gravitational wave transients (GWTC-1) contains 11 signals from black hole and neutron star binaries, the number of observations is increasing rapidly as detector sensitivity improves. To extract information from the observed signals, it is imperative to have fast, flexible, and scalable inference techniques. In a previous paper, we introduced BILBY: a modular and user-friendly Bayesian inference library adapted to address the needs of gravitational-wave inference. In this work, we demonstrate that BILBY produces reliable results for simulated gravitational-wave signals from compact binary mergers, and verify that it accurately reproduces results reported for the 11 GWTC-1 signals. Additionally, we provide configuration and output files for all analyses to allow for easy reproduction, modification, and future use. This work establishes that BILBY is primed and ready to analyse the rapidly growing population of compact binary coalescence gravitational-wave signals.