Audio-visual aids in the high school driver-education program

Thesis (Ed. M.)--Boston University, 195

A Mean Value Internal Combustion Engine Model in MapleSim

The mean value engine model (MVEM) is a mathematical model derived from basic physical principles such as conservation of mass and energy equations. Although the MVEM is based on some simplified assumptions and time averaged combustion engine parameters, it models the engine with a reasonable approximation and gives a satisfactory amount of information about the physics of the fluid energy passing through an engine system. MVEM can predict an engine’s main external variables such as crankshaft speed and manifold pressure, and important internal variables, such as volumetric and thermal efficiencies. Usually, the differential equations used in MVEM will predict fuel film flow, manifold pressure, and crankshaft speed. Because of its simplicity and short simulation time, the MVEM is widely used for engine control development. A mean value engine based on mathematical and parametric equations has recently been developed in the new MapleSim software. The model consists of three main components: the throttle body, the manifold, and the engine. The new MVEM uses combinations of causal and acausal components along with lookup tables and parametric equations. Adjusting the parameters allows the model to be used for new engines of interest. The model is forward-looking and so benefits from both Maple’s powerful mathematical tool and Modelica’s modern equation-based language. A set of throttle angle and mass flow data is used to find the throttle angle function, and to validate the throttle mass flow rates obtained from the model and the experiment

Witness: The Modern Writer as Witness

Editor\u27s Note [Excerpt] The United States, as a society, is on the brink of profound and positive change. Demographically and culturally, things are improving, and the reason is obvious to people who study history: Conflict pushes us to be better, to strive for principled goals. Consider the inspired eco-advocacy of Greta Thunberg. Or the swearing in of most diverse class of lawmakers in history into the 116th Congress. Or billionaire Robert F. Smith’s pledge to pay off every Morehouse College (in Atlanta, Georgia) student’s debt. Indeed, there are many good people helping and great moments happening in spite of a bleak 24-hour news cycle designed to ruin happiness and to limit our understanding of our human potential. We at Witness see this yearning for transformation in the works we selected. The doorway must be crossed, and the voices and characters we featured in our Winter 2019 issue stand at the vestibule, ready for the light to warm them, primed to fight for that necessary illumination.https://digitalscholarship.unlv.edu/witness/1000/thumbnail.jp

The Sources of Innovation

This seminal book has become essential reading for students taking courses in technology management and innovation, and for managers who are responsible for strategic planning. The author shows how the initiative to innovate can come not only form a manufacturer, but also from suppliers, and customers. The author calls these lead users , and the term has come into the business lexicon. A video course based on the book was produced by the MIT TV department and has been distributed to many business schools. The Sources of Innovation has become essential to an understanding of how and where technological innovation takes place. For the first time, the book shows how the assumption that technological innovation is generated by manufacturers is inaccurate. Innovation takes place where it creates the most value, and that can be in a variety of locations, from suppliers to end users. Understanding this fact can facilitate the innovation process, leading to faster and better processes and products

Analysis of Assessment and Hemodynamic Activation in the Prefrontal Cortex: An Investigation of Executive Function

Executive function (EF) refers to the group of cognitive processes that guide human behavior. EF dysfunction is characteristic of a number of clinical conditions such as ADHD. Functional Near-Infrared Spectroscopy (fNIRS) is an economical and less invasive means to image the cortex during tasks of EF to visualize cognitive processes. Measuring hemodynamics in those with and without ADHD during EF tasks, and comparing hemodynamics, EF performance and ratings of EF in daily functioning can yield additional insight into the functional relationship of the cortex and behavior. This study utilized the EXecutive Abilities: Measures and Instruments for Neurobehavioral Evaluation and Research (EXAMINER), Trail Making Test (TMT), Twenty Questions (20Q) task from the Delis-Kaplan Executive Function System (DKEFS), and the Behavior Rating Inventory of Executive Function-Adult (BRIEF-A) Behavior Regulation Index (BRI) and Metacognition Index (MCI). NIRS data was collected during the EF performance tasks and results were calculated based on average hemodynamic responses. Additional questions were addressed regarding the association of EF ratings to EF performance, whether there was an association between digital and non-digital EF tasks, and whether performance differed between those with and without ADHD in terms of hemodynamics and performance or only performance variables. A moderate association was found between BRI and decreased oxygenated hemoglobin (oxyHb) in the left DLPFC during Set Shifting. Higher MCI was moderately associated with decreased oxyHb in the left DLPFC during a task of inhibition and sustained attention, and improved performance on N-Back was moderately associated with increased oxyHb in bilateral DLPFC during Set Shifting. No statistically significant differences were found between ADHD and Non-ADHD groups in PFC hemodynamics during EF tasks; however, ADHD participants exhibited greater impairment on ratings of EF. No statistically significant associations between digital and non-digital tasks were found. Findings confirm deficits in everyday EF in those with ADHD; however, continued use of digital tasks to assess EF constructs, and use of those results for diagnostic purposes is not consistently supported in the literature. Additional information regarding use of EF tasks in those with and without ADHD may provide additional insight into the connection between neurophysiology and everyday function

A novel technique for high-resolution frequency discriminators and their application to pitch and onset detection in empirical musicology

This thesis presents and evaluates software for simultaneous, high-resolution time-frequency discrimination. Whilst this is a problem that arises in many areas of engineering, the software here is developed to assist musicological investigations. In order to analyse musical performances, we must first know what is happening and when; that is, at what time each note begins to sound (the note onset) and what frequencies are present (the pitch). The work presented here focusses on onset detection, although the representation of data used for this task could also be used to track the pitch. A potential method of determining pitch on a sample-to-sample basis is given in the final chapter. Extant software for onset detection uses standard signal processing techniques to search for changes in features like the spectrum or phase. These methods struggle somewhat, as they are constrained by the uncertainty principle, which states that, as time resolution is increased, frequency resolution must decrease and vice versa. However, we can hear changes in frequency to a far greater time resolution than the uncertainty principle would suggest is possible. There is an active process in the inner ear which adds energy and enables this perceptual acuity. The mathematical expression which describes this system is known as the Hopf bifurcation. By building a bank of tuned resonators in software, each of which operates at a Hopf bifurcation, and driving it with audio, changes in frequency can be detected in times that defy the uncertainty relation, as we are not seeking to directly measure the time-frequency features of a system, rather it is used to drive a system. Time and frequency information is then available from the internal state variables of the system. The characteristics of this bank of resonators - called a 'DetectorBank' - are investigated thoroughly. The bandwidth of each resonator ('detector') can be as narrow as 0.922Hz and the system bandwidth is extended to the Nyquist frequency. A nonlinear system may be expected to respond poorly when presented with multiple simultaneous input frequencies; however, the DetectorBank performs well under these circumstances. The data generated by the DetectorBank is then analysed by an OnsetDetector. Both the development and testing of this OnsetDetector are detailed. It is tested using a repository of recordings of individual notes played on a variety of instruments, with promising results. These results are discussed, problems with the current implementation are identified and potential solutions presented. This OnsetDetector can then be combined with a PitchTracker to create a NoteDetector, capable of detecting not only a single note onset time and pitch, but information about changes that occur within a note. Musical notes are not static entities: they contain much variation. Both the performer's intonation and the characteristics of the instrument itself have an effect on the frequency present, as well as features like vibrato. Knowledge of these frequency components, and how they appear or disappear over the course of the note, is valuable information and the software presented here enables the collection of this data