Sea turtles complete migrations across vast distances, covering entire ocean basins. To track these migrations, satellite tracking tags are attached to their shells. The impact of these tags must be considered to ensure that turtles' natural behavior is not artificially and adversely impacted through tag-related drag, and that the data collected by a small sample of sea turtles accurately represents the larger population. Additionally, it can be difficult to study animal energetics in the field over large migration distances. In this work, we modify a computational behavior model to study how satellite tracking tags affect turtle migration behavior. Our agent based model contains synthetic magnetic field environments that are used for navigation cues, an ocean current, resource distributions that represent locations of food, and an agent that attempts to migrate to several different goals. The agent loses energy as it progresses, and searches for the resource distributions to replenish itself. Our novel simulation framework demonstrates the relationship between an agent's available energy capacity, its energy consumption based on mechanical power expended, and its ability to navigate to all migratory goal points. This study can be utilized to (1) probe the impacts of an animal's energy capacity and foraging behavior on its resulting navigation and ecology, (2) guide future satellite tag designs, and (3) develop usage recommendations for a suitable tracking tag based on the type of experiment being conducted. Our model can be expanded beyond sea turtles to study other marine species (e.g., sharks, whales). Additionally, this model could be expanded to other domains within the marine environment. For example, it could be modified to examine design trade-offs in remotely operated vehicles (ROVs), which share many of the same operational constraints as sea turtles and other migratory species.

Motivated by cyber-physical vulnerabilities in precision manufacturing processes, there is a need to externally examine the operational performance of computer numerically controlled (CNC) manufacturing systems. The overarching objective of this work is to design and fabricate a proof-of-concept CNC machine evaluation device, ultimately re-configurable to the mill, lathe, and 3D printing machine classes. This device will assist in identifying potential cyber-physical security threats in manufacturing systems by identifying perturbations outside the expected variations of machining processes and comparing the desired command inputted into the numerical controller and the actual machine performance (e.g., tool displacement, frequency). In this paper, a requirement-driven prototype device design method is presented and tested, and the results will be used to improve future iterations of the design. The first design iteration is tested on a Kuka KR 6 R700 series robotic arm, and machine movement comparisons are performed ex situ using Keyence laser measurement sensors. Data acquisition is performed with a Raspberry Pi 4 microcomputer, controlled by custom, cross-platform python code, and includes a touch screen human-computer interface (HCI). A device design adapted for a CNC mill is also presented, and the Haas TM-2 is used as a case study, which can be operated by CNC operators to assess machine performance, as needed, before a critical manufacturing process. This research will enable the broader goal of recognizing repeating advanced manufacturing performance deviations, which occur outside of a machine's normal variability, that could signal a malicious "attack."

Robust design strategies continue to be relevant during concept-stage complex system design to minimize the impact of uncertainty in system performance due to uncontrollable external failure events. Historical system failures such as the 2003 North American blackout and the 2011 Arizona-Southern California Outages show that decision making, during a cascading failure, can significantly contribute to a failure's magnitude. In this paper, a scalable, model-based design approach is presented to optimize the quantity and location of decision-making agents in a complex system, to minimize performance loss variability after a cascading failure, regardless of where the fault originated in the system. The result is a computational model that enables designers to explore concept-stage design tradeoffs based on individual risk attitudes (RA) for system performance and performance variability, after a failure. The IEEE RTS-96 power system test case is used to evaluate this method, and the results reveal key topological locations vulnerable to cascading failures, that should not be associated with critical operations. This work illustrates the importance of considering decision making when evaluating system level tradeoffs, supporting robust design.

This paper describes the development of a model of an impaired human arm performing a reaching motion, which will be used to predict hand path trajectories for people with reduced arm joint mobility. Assuming that the arm was in contact with a surface during the entire movement, the contact conditions at the initial and final task locations were determined and used to generate the entire trajectory. The model was validated by comparing it to experimental data, which simulated an arm joint impairment by physically constraining the joint motion with a brace. Future research will include using the model in the development of physical training protocols that avoid early recruitment of “healthy” Degrees-OfFreedom (DOF) for reaching motions, thus facilitating an Active Range-Of-Motion Recovery (AROM) for a particular impaired joint. Keywords—Higher order kinematic specifications, human motor coordination, impaired movement, kinematic synthesis.

Hearing-impaired individuals have much more difficulty, then normal hearing individuals, with speech intelligibility in cocktail party situations. Hence, the overall research goal is to improve intelligibility of speech and to enhance spatial hearing of hearing aids in these situations. It is known that spatial audio leads to improved speech intelligibility. As a step towards this goal, source separation algorithms were investigated. In particular, the DUET Blind Source Separation (BSS) algorithm was modified to automatically extract speech sources in a simulated auditory scene.

Head-Related Transfer Functions (HRTFs) are special digital filters used to create the effect of three-dimensional (3D) virtual sound source placement over headphones. The two most common methods of obtaining HRTFs are to either individually measure the HRTFs on specialized equipment (individualized HRTFs) or to create a set of generic HRTFs by measuring them on a mannequin with average anatomical features (generic HRTFs). Individualized HRTFs required specialized equipment that is not readily available to the general public. Additionally, it is known that HRTFs are heavily dependent on our anatomical features. As a result, generic HRTFs produce significant localization errors. A multi-linear model is now available which uses simple anthropometric measurements of the intended user’s anatomy to generate customized HRTFs. These customized HRTFs can be generated without specialized equipment and have improved spatialization over generic HRTFs. However, the anthropometric measurements, which are used as parameters for the customization model, are currently collected manually. In the present work, image processing techniques are used to automatically estimate a portion of the anthropometric measurements of the human pinnae. Analysis of the estimation technique’s performance will also be conducted.

Subjective assessments of noise from aircraft flight operations require time histories of acoustic pressure at listener positions. Synthesized sound has an advantage over recordings by allowing the examination of proposed aircraft, flight procedures, and other conditions or configurations for which recordings are unavailable. Previous work by the authors [J. Acoust. Soc. Am. 113, 2245 (2003); 114, 2340 (2003); 116 2515 (2004)] focused on the development of a two-stage process for simulating flyover noise. The first stage entailed synthesizing the time histories at the flying source based on predictions of the source directivity. A second real-time rendering stage entailed propagation to a listener position on the ground, with binaural playback over headphones. More recently, loudspeaker based rendering was implemented for three-dimensional presentation in the NASA Langley Exterior Effects Room [J. Acoust. Soc. Am. 128, 2482 (2010); POMA 9, 015004 (2010)]. This paper discusses recent developments to the synthesis and rendering stages, including a new technique for sample-based synthesis of rotary wing sources, improvements to fan noise synthesis, and enhancements to ground plane simulation.

The Exterior Effects Room (EER), located at the NASA Langley Research Center, is a facility built for psychoacoustic studies of aircraft community noise. Recently, the EER was significantly upgraded to allow for simulation of aircraft flyovers in a 3-D audio and visual environment. The upgrade included installation of 27 satellite and 4 subwoofer loudspeakers that are driven by a real-time audio server. The audio server employs an implementation of the vector base amplitude panning method to position virtual sources at arbitrary azimuth and elevation angles in the EER. Real-time application of filters, time delays, and gains are required to compensate for installation effects, including those associated with the irregular room geometry, colorization due to varying loudspeaker installations, and crossover filtering. The authors previously showed [J. Acoust. Soc. Am., 127, 1969 (2010)] that color compensation and crossover filtering could be achieved for satellite and subwoofer loudspeakers. However, the resulting FIR filters were too long (32 768 taps) to implement in real-time. The focus of this work is on the development of reduced-length surrogate IIR filters and on the measurement of the acoustic performance of the installed real-time system.

There are currently three options for the implementation of spatial audio systems based on head-related transfer functions (HRTFs) individually measured HRTFs, genetic HRTFs, and customizable HRTFs. Individualized HRTFs require the subject to undergo lengthy measurements in and anechoic chamber under the supervision of trained personnel, which limits their availability to the average potential user. To overcome this, many researchers and commercial developers have resorted to using genetic HRTFs. However, it has been shown that genetic HRTFs result in an increase in localization errors The third possibility, which we are pursuing, is the customization HRTFs. In which the anatomical measurements of a potential user are utilized to determine the parameters of a structural model of the head and the pinnae However, an initial step of decomposing the impulse responses of individuals HRTFs into a summation of damped and delayed sinusoids (DDSs) is needed to reveal the parameters of the structural pinna model An exhaustive search decomposition method has already been developed which seems to do this accurately when the DDSs are separated by long latencies, but not for short latencies The application of the Hankel total least-squares (HTLS) decomposiiton method is proposed as a solution to the parameter extraction problem faced in customizing HRTFs when short latencies are expected between the DSSs that make up their impulse response

The Exterior Effects Room (EER) at the NASA Langley Research Center is a 39-seat auditorium built for psychoacoustic studies of aircraft community noise. The original reproduction system employed monaural playback and hence lacked sound localization capability. In an effort to more closely recreate field test conditions, a significant upgrade was undertaken to allow simulation of a three-dimensional audio and visual environment. The 3D audio system consists of 27 mid and high frequency satellite speakers and 4 subwoofers, driven by a real-time audio server running an implementation of Vector Base Amplitude Panning. The audio server is part of a larger simulation system, which controls the audio and visual presentation of recorded and synthesized aircraft flyovers. The focus of this work is on the calibration of the 3D audio system, including gains used in the amplitude panning algorithm, speaker equalization, and absolute gain control. Because the speakers are installed in an irregularly shaped room, the speaker equalization includes time delay and gain compensation due to different mounting distances from the focal point, filtering for color compensation due to different installations (half space, corner, baffled/unbaffled), and cross-over filtering.

Spatialized ("3D") audio may be useful as an alternative sensory channel to provide blind individuals with relevant spatial information in the physical world or while interacting with computers. However, an important limitation of this approach is the lower spatial resolution achievable through sound localization. While this limitation is widely acknowledged, few empirical evaluations of the sound localization achievable through audio spatialization techniques have been performed, particularly with respect to elevation localization. We performed such an empirical study and found quantitative confirmation that the localization accuracy deteriorates as the virtual sound position is set farther above or below the ear (height) level. This information may be valuable to HCI designers planning to use 3D sound.

Currently, achieving high-fidelity sound spatialization requires the prospective user to undergo lengthy measurements in an anechoic chamber using highly specialized equipment. This, in turn, has increased the cost and reduced the availability of high-fidelity spatialization to the general public. Attempts to generalize 3D audio have been made using the measurement of a KEMAR dummy head or creating a database containing a sample of the public. Unfortunately, this leads to increased front/back reversals and localization errors in the median plane. Customizable head-related impulse responses (HRIRs) would reduce the errors caused by general HRIRs and remove the limitation of the measured HRIRs. This article reports an initial stage in the development of customizable HRIRs. The ultimate goal is to develop a compact functional model that is equivalent to empirically measured HRIRs but requires a smaller number of parameters that could be obtained from the anatomical characteristics of the intended listener. In order to arrive at such a model, the HRIRs must be decomposed into multiple-scaled and delayed-damped sinusoids, which would reveal the parameters that the compact model needs to have an impulse response similar to the measured HRIR. Previously this type of HRIR decomposition has been accomplished through an exhaustive search of the model parameters. A new method that approaches the decomposition simultaneously in the frequency (Z) and time domains is reported here.

Head-Related Transfer Functions (HRTFs) are used to achieve binaural sound spatialization. Originally, sound spatialization techniques that utilize HRTFs require an intended listener to undergo lengthy measurements with specialized equipment. Unfortunately, the alternative generic HRTFs increase localization errors especially in elevation. Another option that we are pursuing is to customize HRTFs based on the physical measurements of a listener such that their performance is equivalent to measured HRTFs. However, an initial step of decomposing measured HRTFs in order to reveal the parameters of the required structural pinna model must be performed. A new approach for the decomposition of HRTFs is suggested and evaluated on simulated examples. Finally, the method is used to decompose actual HRTFs and the results are evaluated.

The pupil diameter (PD) is adjusted by muscles under the control of the sympathetic and parasympathetic divisions of the Autonomic Nervous System (ANS). It is well known that a major mechanism for PD adjustment is the pupillary light reflex (PLR), by which the PD decreases as a response to increased illumination on the retina. However, it has also been found that PD is modified by ANS changes due to affective variations in the subject. Ultimately, we pursue the reproduction and cancellation of PLR-driven PD changes from the PD signal as measured during human-computer interaction, using an Adaptive Interference Canceller (AIC), so that the PD changes not driven by PLR may be used to gauge the affective changes of the computer user. As a preliminary step towards that goal we studied the actual performance of an AIC in modeling changes in measured PD caused exclusively by changes in illumination, which were simultaneously recorded by a light sensor. Our results confirm that the AIC was able to converge, minimizing its error to acceptable levels for all of our 8 subjects. Furthermore, the impulse response model implicitly formed in the weights of the adapted AIC seemed to generally coincide with the impulse response that previous research by others would predict for the transfer function mediating between changes in illumination and changes of pupil diameter.

In order to achieve highly accurate 3D audio spatialization, an intended listener must undergo measurements in a specialized instrumentation system to obtain "individual" Head-Related Impulse Responses (HRIRs). Our goal is to create a customizable sound spatialization system that would not require complex individual measurements. Our approach is based on the reliable decomposition of measured HRIRs into damped sinusoidals. A previously developed method showed good performance in decomposing HRIRs collected by us at a sampling frequency of 96 kHz, but other HRIR databases comprise HRIRs recorded at lower rates. This paper compares the performance of our automated decomposition method with sampling rates of 96 kHz and 48 kHz.

Currently, to obtain maximum fidelity 3D audio, an intended listener is required to undergo time consuming measurements using highly specialized and expensive equipment. Customizable Head-Related Impulse Responses (HRIRs) would remove this limitation. This paper reports our progress in the first stage of the development of customizable HRIRs. Our approach is to develop compact functional models that could be equivalent to empirically measured HRIRs but require a much smaller number of parameters, which could eventually be derived from the anatomical characteristics of a prospective listener. For this first step, HRIRs must be decomposed into multiple delayed and scaled damped sinusoids which, in turn, reveal the parameters (delay and magnitude) necessary to create an instance of the structural model equivalent to the HRIR under analysis. Previously this type of HRIR decomposition has been accomplished through an exhaustive search of the model parameters. A new method that approaches the decomposition simultaneously in the frequency (Z) and time domains is reported here.