PhD, University of Cincinnati, 2024, Engineering and Applied Science: Electrical Engineering

Echolocating bats demonstrate sonar's remarkable ability to support various complex tasks, from navigating dense vegetation to foraging in flight. However, achieving these tasks using echolocation alone presents challenges due to the inherent limitations of sonar. Drawing inspiration from biological systems, we can narrow the performance gap between artificial and biological sonar. In this context, we develop behavior-based systems using sensorimotor loops and their interactions to accomplish the foraging task. We showcase a solution to the foraging task by training machine learning models to arbitrate between the approach and avoid sensorimotor loops using solely echoes as perception inputs. These models demonstrate a high success rate in foraging for insect-like targets and flower targets. In addition, they are robust against different arena geometries, traps, and noisy motor control. While arbitrating between the two sensorimotor loops proves effective, the system does not demonstrate attention to a single target, failing to investigate a flower target thoroughly. To address this, we develop a novel sensorimotor loop, termed the flower home-in loop, designed to guide nectarivorous bats toward a flower opening while maintaining attention on the target. The flower home-in loop perfectly performs when approaching the flower from the front. However, the performance gets worse when approaching the flower from other sides. Continuously re-estimating the target flower pose and updating the bat's path is a more robust strategy for the flower home-in loop in acoustically ambiguous scenarios. The flower home-in loop can also guide the bat toward the echoes from a flower's opening while being interfered with by echoes from nearby objects.

Committee: Dieter Vanderelst Ph.D. (Committee Chair); Ali Minai Ph.D. (Committee Member); Herbert Peremans Ph.D M.A B.A. (Committee Member); John Gallagher Ph.D. (Committee Member); Zachariah Fuchs Ph.D. (Committee Member) Subjects: Robotics

Master of Science (MS), Ohio University, 2022, Biological Sciences (Arts and Sciences)

The arrival of white-nose syndrome (WNS) in North America has exposed large gaps in our understanding of bat ecology, highlighting the importance of understanding species' natural history. As the need to locate and protect remnant bat populations has increased, it has become apparent that important winter habitats have been overlooked. To improve our understanding of overwintering bat communities and their habitats, I deployed acoustic bat detectors outside potential hibernacula during autumn and winter. I aimed to determine if visual surveys of traditional (caves, mines, and railroad tunnels) and non-traditional (cliffs and rock outcrops) hibernacula underrepresent bat species richness at the site and used model selection to determine what suite of variables best predicted winter activity (Chapter 1). I also used model selection to determine if external characteristics of abandoned underground mines predicted swarming activity during autumn (Chapter 2). I found that winter acoustic surveys detected greater bat species richness outside both traditional and non-traditional hibernacula than visual surveys. In addition, I found that daily temperature and precipitation influenced activity of all species, with the most activity occurring on relatively warmer nights with little precipitation. Temperature also positively influenced activity of big brown bats (Eptesicus fuscus), tricolored bats (Perimyotis subflavus), and Myotis species outside of abandoned mines during autumn. Activity of Myotis species was positively influenced by shorter and wider mine entrance shape, tricolored bat activity was positively influenced by larger mine entrance areas and lower densities of potential hibernacula, and big brown bat activity was positively influenced by larger underground mine extents. These findings can provide insight to locate, better evaluate, and conserve vital overwintering bat habitat.

Committee: Joseph Johnson (Advisor) Subjects: Acoustics; Animal Sciences; Animals; Biology; Ecology; Wildlife Conservation; Wildlife Management; Zoology

MS, University of Cincinnati, 2019, Engineering and Applied Science: Electrical Engineering

Sonar-based navigation systems have been immensely used for over a century. Underwater navigation is primarily achieved using Sonar, as Laser-based systems are largely ineffective due to the darkness of the oceans. However, the use of Sonar waves for navigation other than underwater has been very limited. The rise in demands of autonomous systems like self-driving cars and unmanned aerial vehicles have posed new challenges in designing sensor systems. Sonar-based sensors, with the unique properties of sound waves, present themselves as a possible solution, especially in scenarios where visibility is heavily compromised by smoke, dust or other suspended particles. The motivation for using Sonar arises from the flight pattern of bats. Bats are able to effectively navigate in dense vegetation using echolocation and are able to map surroundings for which other animals rely on their vision. In this thesis, we present the design of a data acquisition system with the ability to simulate bat echolocation by generating sonar-waves and recording the resulting echoes. The first part of the thesis focuses on the hardware circuit design for the ultrasonic peripherals. A software interface for the hardware circuit is developed which allows control and flexibility over the hardware not possible on existing sonar-based solutions in the market. To evaluate the practicality of the sonar-based sensor, we design an experimental setup to quantify its performance using resultant echoes, in conditions where visibility is compromised. An artificially generated fog and a LIDAR-based sensor are used to simulate the environment. Experiments are carried out to determine the effect of distance and fog density on the sensor's performance. The simulated environment and the sonar-board are also used to determine the response of different frequencies in the sonar spectrum. We conclude that the sonar-based board can be effectively used in environments with very high levels of fog to detect obj (open full item for complete abstract)

Committee: Ranganadha Vemuri Ph.D. (Committee Chair); Wen-Ben Jone Ph.D. (Committee Member); Dieter Vanderelst Ph.D. (Committee Member) Subjects: Electrical Engineering

Doctor of Philosophy, University of Toledo, 2014, Engineering

Many birds and bats fatalities have been reported in the vicinity of wind farms. An acoustic, infrared camera, and marine radar based system is developed to monitor the nocturnal migration of birds and bats. The system is deployed and tested in an area of potential wind farm development. The area is also a stopover for migrating birds and bats. Multi-sensory data fusion is developed based on acoustics, infrared camera (IR), and radar. The diversity of the sensors technologies complicated its development. Different signal processing techniques were developed for processing of various types of data. Data fusion is then implemented from three diverse sensors in order to make inferences about the targets. This approach leads to reduction of uncertainties and provides a desired level of confidence and detail information about the patterns. This work is a unique, multifidelity, and multidisciplinary approach based on pattern recognition, machine learning, signal processing, bio-inspired computing, probabilistic methods, and fuzzy reasoning. Sensors were located in the western basin of Lake Erie in Ohio and were used to collect data over the migration period of 2011 and 2012. Acoustic data were collected using acoustic detectors (SM2 and SM2BAT). Data were preprocessed to convert the recorded files to standard wave format. Acoustic processing was performed in two steps: feature extraction, and classification. Acoustic features of bat echolocation calls were extracted based on three different techniques: Short Time Fourier Transform (STFT), Mel Frequency Cepstrum Coefficient (MFCC), and Discrete Wavelet Transform (DWT). These features were fed into an Evolutionary Neural Network (ENN) for their classification at the species level using acoustic features. Results from different feature extraction techniques were compared based on classification accuracy. The technique can identify bats and will contribute towards developing mitigation procedures for reducing bat fata (open full item for complete abstract)

Committee: Mohsin Jamali Dr. (Committee Chair); Jackson Carvalho Dr. (Committee Member); Mohammed Niamat Dr. (Committee Member); Richard Molyet Dr. (Committee Member); Mehdi Pourazady Dr. (Committee Member) Subjects: Biology; Computer Engineering; Computer Science; Ecology; Electrical Engineering; Energy; Engineering

Master of Science (MS), Ohio University, 2008, Environmental Studies (Arts and Sciences)

Bats are an ideal group of species to evaluate the effect of land management practices because their nesting and feeding habitats are often different and spatially separate. The presence or absence of bat species may indicate habitat quality. The use of different habitat types by different bat species was detected using echolocation calls. Five bat species were detected in SE Ohio. Myotis lucifugus was found in significantly higher numbers in the areas of Old Growth forest that areas thinning into Pasture habitat (eco-tone). M. lucifugus was also found to have significantly higher counts of feeding buzzes in this habitat type. Eptesicus fuscus was found in significantly higher numbers in the Pasture habitat it was also found to be hunting in significantly high numbers in the Pasture habitat. Lasionycteris noctivagans was found in significantly high numbers in the Old Growth, Eco-tone, and Pasture habitats. L. noctivagans was not significantly found to hunt in any particular habitat type.

Committee: Donald Miles (Advisor) Subjects: Biology, Ecology

Master of Science (MS), Ohio University, 1991, Electrical Engineering & Computer Science (Engineering and Technology)

A new application of digital spectrum estimation algorithms is presented. The frequency content of ultrasonic echolocation signals emitted by various bat species are investigated. Using several different analysis algorithms, an empirical determination of the most suitable technique for this class of waveform is reached. In addition, interspecies and intraspecies classification of bats utilizing the most appropriate estimators is investigated for feasibility. The motivation for this research arose from a need in the zoological community for faster, more efficient, and more accurate analysis tools. Using computers and digital spectral estimation techniques, in conjunction with a high-speed analog-to-digital converter and broadband microphone, spectral information could be obtained and analyzed faster, and more efficiently due to the elimination of unnecessary or inappropriate processing. Results showed that the most appropriate analysis algorithm for these waveforms was dependent on the spectral properties of the particular signal. The FFT with a Kaiser window applied was shown to be very useful, and the Autoregressive parametric estimator with an approximate system order of 8 to 10 also proved to be advantageous in certain situations. However, the conclusion is reached that accurate spectral interpretation will require analysis over several time bins in the signal, in order that frequency information with respect to time can be obtained. The spectra produced by the estimators indicated surprisingly distinguishable characteristics with peaks that were easily discernable. Past research findings regarding spectral shape of echolocation bursts were supported, and the implication is that classification of species or even individuals is possible. With some modification and enhancement, the system could be contained in a portable computer, allowing use in remote locations where classification is the primary intent.

Committee: Jeff Giesey (Advisor) Subjects:

Master of Science in Engineering (MSEgr), Wright State University, 2013, Electrical Engineering

Some blind humans have developed the remarkable capability of echolocation, similar to the type used by mammals such as the bat, dolphin and whale. This population of human has shown the ability to classify targets based on their location, size, shape and material in diverse environmental conditions simply by listening to the reflected echoes of tongue clicks generated by their mouth. To date, much of the research into human echolocation has been confined exclusively to behavioral science and the analysis is inconsistent with the approaches used in engineering. The waveforms used in current radar systems appear different to those typical of mammal echolocation. It is speculated that the lack of robust success in radar target recognition may therefore be attributed to application of an inappropriate waveform. This research focuses on the analysis of human echolocation waveforms and their reflected echoes from different objects to investigate what properties of the waveform may carry target information. Results based on the analyses of echo data collected for various targets and their extracted features suggests that normalized target signatures cannot provide target classification in efficient manner. The normalized frequency spectrum has some potential for target classification, but it does not lead to confident classification results. The absolute difference between normalized frequency spectrum of transmit signal and normalized frequency spectrum of echoes performs much better than the two features discussed previously. It should be noted that the tongue click waveform performs much better at classifying objects made of hard materials from objects made of soft materials. However, they cannot be classified based on their shape or size by utilizing this feature. The chirp waveform provides superior classification performance for this feature, however, it is unclear which broad categories the targets can be put in for classification. The chirp, certainly, cannot c (open full item for complete abstract)

Committee: Arnab Shaw Ph.D. (Advisor); Chris Baker Ph.D. (Committee Member); Fred Garber Ph.D. (Committee Member) Subjects: Electrical Engineering