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Recent advances in technology permitted the first simultaneous digital recording of intranarial pressure and on-axis acoustic data from bottlenose dolphins during a biosonar target recognition task. Analysis of pressurization events in the intranarial space quantifies and supports earlier work, confirming that intranarial pressure is increased when whistle vocalizations are emitted. The results show complex relationships between various properties of the biosonar click to the intranarial pressure difference at the time it was generated. The intranarial pressure that drives the production of clicks is not the primary determinant of many of the acoustic properties of those clicks. A simple piston-cylinder physical model coupled with a sound production model of clicks produced at the monkey-lips/dorsal bursae complex yields an estimate of mechanical work for individual pressurization events. Individual pressurization events are typically associated with a single click train. Mechanical work for an average pressurization event is estimated at 10 Joules.

Bats are noted for having extremely powerful biosonar systems that enable them to move through and hunt through the thick foliage. They have a single emitter (mouth or nose) and two receivers in their biosonar system (ears). Some bat species, such as those belonging to the group's rhinophid and hipposiderid, feature intricate pinna motion patterns. These pinnae are divided into two groups: stiff movements and non-rigid motions. To understand how pinna sense worked has been studied in this thesis. The rigid pinna movements displayed a significantly different rotation, with revolutions axes spanning 180° in horizontal and curvature, according to axis-angle representations. The classification of landmarks on the pinna surface has explained two types of non-stiffed pinna movements. Additionally, a bio-inspired pinna has been used to explore the acoustic impact of the stiff pinna movements. All the given results showed precise accuracy in the motion of variance bats pinnae. The research initiative was initiated with a comprehensive exploration of various design concepts, primarily focused on elucidating the intricate interplay between actuator geometry and the resultant deformation of the pinna. Employing a structured design code facilitated the generation of an array of configurations, each subject to stringent conditions and parameter settings necessitating subsequent validation. After this design exploration, a tri-tiered hierarchy of forces, encompassing nominal, intermediate, and elevated magnitudes, was applied to instigate a systematic optimization process aimed at determining the most favorable deformation pattern. Computational simulations leveraging Finite Element Analysis (FEA) were conducted, accompanied by a rigorous material characterization procedure, to effectively quantify the extent of deformation across the array of configurations. A consequential phase of the investigation involved the implementation of Principal Component Analysis (PCA) to differentiate the inherent variability within the different deformation arrangements, shedding light on their relative structural and morphological distinctions. The culmination of the study encompassed the utilization of the Genetic Algorithm (GA), a sophisticated optimization technique, to facilitate the fine-tuning of deformation patterns in pursuit of the overarching goal: the deliberate induction of substantial and diverse variations in pinna morphology. In summary, the research trajectory progressed sequentially through design conceptualization, force-induced optimization, computational simulations incorporating FEA and material characterization, Variability analysis via PCA, and culminated in the deployment of the GA to achieve the prime objective of inducing pronounced variability in pinna configuration. The work was done as following, starting with design concepts, the main benefit of this is to understand how the geometry of actuator affects the pinna deformation. Using the design code to present several configurations that must have conditions and parameters to be validated. After that applying 3 different forces (zero, medium, and high) to get the optimization for pattern. Applying the FEA simulations with help of material characterization to display the displacement of the arrangements. Finally doing the Variability analysis by using the principal component analysis. Then concluding the work by using the Genetic algorithm for optimizations to reach the main goal which is large variability in the pinna shape. / Doctor of Philosophy / This research delves into the fascinating world of bats and their extraordinary biosonar systems, specifically focusing on the intricate mechanics of their pinnae—the external ear structures. Bats, known for their remarkable ability to navigate dense foliage using biosonar, have been a subject of keen scientific interest. The study explores the design and functionality of bat pinnae, with a special emphasis on understanding how different movements contribute to their biosonar capabilities. The investigation began with a comprehensive exploration of design concepts, aiming to unravel the complex relationship between actuator geometry and pinna deformation. A structured design code was employed to generate a range of configurations, each subjected to stringent conditions and parameters, requiring subsequent validation. Following this design exploration, a three-tiered hierarchy of forces—ranging from nominal to elevated magnitudes—was applied to initiate a systematic optimization process. Computational simulations, utilizing Finite Element Analysis (FEA) and rigorous material characterization, were conducted to quantify the extent of pinna deformation across various configurations. The study further implemented Principal Component Analysis (PCA) to discern inherent variability in different deformation patterns, shedding light on their structural and morphological distinctions. The research culminated in the deployment of the Genetic Algorithm (GA), a sophisticated optimization technique, to deliberately induce substantial and diverse variations in pinna morphology. In summary, the research trajectory progressed from design conceptualization to force-induced optimization, incorporating computational simulations and material characterization. Variability analysis through PCA provided insights into structural distinctions, and the use of the Genetic Algorithm aimed at achieving the overarching goal of inducing pronounced variability in pinna configuration. This work not only enhances our understanding of bat biosonar systems but also offers potential applications in bio-inspired design and acoustic engineering.

The evolution of bats is characterized by a combination of two key innovations - powered flight and biosonar - that are unique among mammals. Bats still outperform engineered systems in both capabilities by a large margin. Bat biosonar stands out for its ability to encode and extract sensory information using various mechanisms such as adaptive beam width control, dynamic sound emission and reception, as well as cognitive processes. Due to the highly integrated and sophisticated design of their active sonar system, bats can survive in complex and dense environments using just a few simple smart acoustic elements. On the sound emission side, significant features that distinguish bats from the current man-made sonar system are the time-variant shapes of the noseleaves. Noseleaves are baffles that surround the nostrils in bats with nasal pulse emission such as horseshoe bats and can undergo non-rigid deformations large enough to affect their acoustic properties significantly. Behavioral studies have shown that these movements are not random byproducts, but are due to specific muscular action. To understand the underlying physical and engineering principles of the dynamic sensing in horseshoe bats, two experimental prototypes ,i.e. intact noseleaf and simplified noseleaf, have been used. We have integrated techniques of data acquisition, instrument control, additive manufacturing, signal processing, airborne acoustics, 3D modeling and image processing to facilitate this research. 3D models of horseshoe bat noseleaves were obtained by tomographic imaging, reconstructed, and modified in the digital domain to meet the needs of additive manufacturing prototype. Nostrils and anterior leaf were abstracted as an elliptical outlet and a concave baffle in the other prototype. As a reference, a circular outlet and a straight baffle designed. A data acquisition and instrument control system has been developed and integrated with transducers to characterize the dynamic emission system acoustically as well as actuators for recreating the dynamics of the horseshoe bat noseleaf. A conical horn and tube waveguide was designed to couple the loudspeaker to the outlet of bat noseleaf and simplified baffles. A pan-tilt was used to characterize the acoustic properties of the deforming prototypes over direction. By using those techniques, the dynamic effect of the noseleaf was reproduced and characterized. It was suggested that the lancet rotation induced both beam-gain and beamwidth changes. Narrow outlet produced an isotropic beampattern and concave baffle had a significant time-variant and frequency-variant effect with just a small displacement. All those results cast light on the possible functions of the biological morphology and provided new thoughts on the engineering device's design. / Ph. D.

Bats possess remarkably sophisticated biosonar systems that seamlessly integrate the physical encoding of information through intricate ear motions with the neural extraction and processsing of sensory information. While previous studies have endeavored to mimic the pinna (outer ear) dynamics of bats using fixed deformation patterns in biomimetic soft-robotic sonar heads, such physical approaches are inherently limited in their ability to comprehensively explore the vast actuation pattern space that may enable bats to adaptively sense across diverse environments and tasks.To overcome these limitations, this thesis presents the development of deep regression neural networks capable of predicting the beampattern (acoustic radiation pattern) of a soft-robotic pinna as function of its actuator states. The pinna model geometry is derived from a tomographic scan of the right ear of the greater horseshoe bat (textit{Rhinolophus ferrumequinum}. Three virtual actuators are incorporated into this model to simulate a range of shape deformations. For each unique actuation pattern producing a distinct pinna shape conformation, the corresponding ultrasonic beampattern is numerically estimated using a frequency-domain boundary element method (BEM) simulation, providing ground truth data. Two neural networks architectures, a multilayer perceptron (MLP) and a radial basis function network (RBFN) based on von Mises functions were evaluated for their ability to accurately reproduce these numerical beampattern estimates as a function of spherical coordinates azimuth and elevation. Both networks demonstrate comparably low errors in replicating the beampattern data. However, the MLP exhibits significantly higher computational efficiency, reducing training time by 7.4 seconds and inference time by 0.7 seconds compared to the RBFN. The superior computational performance of deep neural network models in inferring biomimetic pinna beampatterns from actuator states enables an extensive exploration of the vast actuation pattern space to identify pinna actuation patterns optimally suited for specific biosonar sensing tasks. This simulation-based approach provides a powerful framework for elucidating the functional principles underlying the dynamic shape adaptations observed in bat biosonar systems. / Master of Science / The aim is to understand how bats can dynamically change the shape of their outer ears (pinnae) to optimally detect sounds in different environments and for different tasks. Previous studies tried to mimic bat ear motions using fixed deformation patterns in robotic ear models, but this approach is limited. Instead this thesis uses deep learning neural networks to predict how changing the shape of a robotic bat pinna model affects its acoustic beampattern (how it radiates and receives sound). The pinna geometry is based on a 3D scan of a greater horseshoe bat ear, with three virtual "actuators" to deform the shape. For many different actuator patterns deforming the pinna, the resulting beampattern is calculated using computer simulations. Neural networks ( multilayer perceptron and radial basis function network) are trained on this data to accurately predict the beampattern from the actuator states. The multilayer perceptron network is found to be significantly more computationally efficient for this task. This neural network based approach allows rapidly exploring the vast range of possible pinna actuations to identify optimal shapes for specific biosonar sensing tasks, shedding light on principles of dynamic ear shape control in bats.

Bats are famous for their capability of navigating in dense forests for hundreds of kilometers within one night by using their sonar system. Airborne sonar hasn't been heavily used in the industrial world compared to other sensors such as lidar, radar, and cameras. In this study, we applied a biosonar robot to navigate in a dense forest with bat-like FM-CF ultrasonic signals with deep learning. The results presented show that airborne biosonar can classify different areas' plants, in addition to achieving a similar level of navigation granularity compared to GPS, which is about 6 meters of radius resolution. The time- frequency representations of echoes from the forest are used as input data to explore the biosonar navigation ability, and the state-of-the-art CNN deep network (Resnet 152) is used as the brain to do the echolocation in the dense forest. The navigation ability can be improved significantly by combining multiple 10 ms long echoes, however, the data size of the reflected waves is much smaller than the other popularly used sensors, as echo can be collected at a rate of 40 echoes per second. The results can prove that airborne sonar can be used to navigate in GPS-denied environments, and can be an important sensor used in a scenario when other sensors meet constraints, like in the sensor fusion applications. / Doctor of Philosophy / The ability to identify natural landmarks could contribute to the navigation skills of echolo- cating bats and also advance the quest for autonomy in natural environments with man- made systems. The critical sensors used in autonomous robot navigation are camera array, radar, and lidar, airborne sonar hasn't been verified for its navigation efficiency. However, recognizing natural landmarks based on biosonar echoes has to deal with the unpredictable nature of echoes that are typically superpositions of contributions from many different reflec- tors with unknown properties. This dissertation intends to explore the bioinspired airborne sonar navigation ability in dense natural forests. The first part of this project is to use reflected echoes to navigate on a large scale, data was collected from different mountains which are dozens of kilometers away from each other, and we achieved the use of one single navigator in those locations. The second part is to explore the navigation granularity of airborne sonar sensors, data were collected from a small dense forest area, we try to classify which part of the foliage was based on the echo, and in the end, we achieved GPS accuracy for navigation. The finding in this work proves that the sonar sensor can play an important role in the sensing system, with the help of a deep neural network, with a 10 ms long echo, it can have a similar navigation ability to GPS.

Horseshoe bats (family Rhinolophidae) are among the bat species that dynamically deform their reception baffles (pinnae) and emission baffles (noseleaves) during signal reception and emissions, respectively. These dynamics are a focus of prior studies that demonstrated that these effects could introduce time-variance within emitted and received signals. Recent lab based experiments with biomimetic hardware have shown that these dynamics can also inject time-variant signatures into echoes from simple targets. However, complex foliage echoes, which comprise a large portion of the received echoes and contain useful information for these bats, have not been studied in prior research. We used a biomimetic sonarhead which replicated these dynamics, to collect a large dataset of foliage echoes (>55,000). To generate a neuromorphic representation of echoes that was representative of the neural spikes in bat brains, we developed an auditory processing model based on Horseshoe bat physiological data. Then, machine learning classifiers were employed to classify these spike representations of echoes into distinct groups, based on the presence or absence of dynamics' effects. Our results showed that classification with up to 80% accuracy was possible, indicating the presence of these effects in foliage echoes, and their persistence through the auditory processing. These results suggest that these dynamics' effects might be present in bat brains, and therefore have the potential to inform behavioral decisions. Our results also indicated that potential benefits from these effects might be location specific, as our classifier was more effective in classifying echoes from the same physical location, compared to a dataset with significant variation in recording locations. This result suggested that advantages of these effects may be limited to the context of particular surroundings if the bat brain similarly fails to generalize over variation in locations. / Master of Science / Horseshoe bats (family Rhinolophidae) are an echolocating bat species, i.e., they emit sound waves and use the corresponding echoes received from the environment to gather information for navigation. This species of bats demonstrate the behavior of deforming their emitter (noseleaf), and ears (pinna), while emitting or receiving echolocation signals. Horseshoe bats are adept at navigating in the dark through dense foliage. Their impressive navigational abilities are of interest to researchers, as their biology can inspire solutions for autonomous drone navigation in foliage and underwater. Prior research, through numerical studies and experimental reproductions, has found that these deformations can introduce time-dependent changes in the emitted and received signals. Furthermore, recent research using a biomimetic robot has found that echoes received from simple shapes, such as cube and sphere, also contain time-dependent changes. However, prior studies have not used foliage echoes in their analysis, which are more complex, since they include a large number of randomly distributed targets (leaves). Foliage echoes also constitute a large share of echoes from the bats' habitats, hence an understanding of the effects of the dynamic deformations on these foliage echoes is of interest. Since echolocation signals exist within bat brains as neural spikes, it is also important to understand if these dynamic effects can be identified within such signal representations, as that would indicate that these effects are available to the bats' brains. In this study, a biomimetic robot that mimicked the dynamic pinna and noseleaf deformation was used to collect a large dataset (>55,000) of echoes from foliage. A signal processing model that mimicked the auditory processing of these bats and generated simulated spike responses was also developed. Supervised machine learning was used to classify these simulated spike responses into two groups based on the presence or absence of these dynamics' effects. The success of the machine learning classifiers of up to 80% accuracy suggested that the dynamic effects exist within foliage echoes and also spike-based representations. The machine learning classifier was more accurate when classifying echoes from a small confined area, as compared to echoes distributed over a larger area with varying foliage. This result suggests that any potential benefits from these effects might be location-specific if the bat brain similarly fails to generalize over the variation in echoes from different locations.

Foliage is very common feature in the habitats of echolocation bats and thus its echoes constitute the major input of bats' sensory systems. Acquiring useful information from vegetation echoes facilitates the bats significantly in the navigation and foraging behaviors. To better understand the foliage echoes, in this dissertation, a computer model was constructed to simulate foliage echoes with following simplifications: approximating leaves as circular disks, leaving out shading effects between leaves, and distributing leaves uniformly in the space. Then one tree can be described with three parameters in the model, leaf radius, orientation, and leaf density, where the first two determine the beampattern of each leaf. Compared with echoes collected from real trees, the simulation echoes are qualitatively accurate, i.e., they match in waveforms and also first-order statistics. Since the ground truth is known in the model, the three parameters were estimated with lasso model by selecting 40 features from each echo. The results have shown that estimation of one parameter with the other two known is usually successful with coefficient of determination close to one, and the classification still has reasonable accuracy when the number of known parameter is reduced to one. Besides, the three simplifications were examined with both experimental and simulation approaches. To assess the acoustic impact of leaf geometry on individual leaves, experiments were carried out by ensonifying leaves from both a single and different species. How the leaves' impulse responses change according to their equivalent radii was investigated. The simulation model of disks fits the experiments done with real leaves within one species and across species reasonably well. Shading effect is found to exist locally when two disks were 25 cm apart and were both in pulse direction. In addition, the inhomogeneous distribution of leaves was introduced by using the branching patterns of L-system. The evaluation of inhomogeneity in echoes produced with two distributions shows that there is always inhomogeneity in echoes, and L-system model does bring more inhomogeneity but not to the same extent as changes in the relative orientation between sonar beam and foliage do. / Ph. D. / Echolocating bats use ultrasonic waves to navigate and forage at night in the forest. They constantly emit pulses and analyze the returning echoes to perceive the surroundings. Foliage echoes are common and important input of their sensory systems, yet what accessible information is contained in foliage echoes for bats is not fully understood. Hence, this dissertation has built an efficient computer model to compute vegetation echoes. To simplify the problem, leaves with various shapes were approximated as circular disks. Besides, every leaf was assumed to be “visible” to sonar, in other words, even if one leaf was shaded by another in the pulse direction, it can still interact with sonar as if the front leaf didn’t exist. Then the leaves were uniformly distributed in the space. With the simplifications above, foliage can be described with three parameters, mean leaf radius, orientation, and leaf density. By varying the three parameters to match features of different trees, a large amount of echoes can be calculated efficiently. Compared with measured echoes from real trees, the simulation echoes are similar with them in terms of waveforms and probability density functions. If producing echoes with two parameters fixed and the third randomly chosen from certain range, the random parameter can be estimated with a linear model, lasso regression model, by extracting features from the echoes as inputs. The estimation is accurate. But if varying one of the two known parameters, the accuracy of estimation is largely reduced. Besides, the three simplifications were examined if they have impact on the simulation results. Impulse responses from leaf specimens were measured with a bio-mimetic sonar head in the anechoic chamber where noise and unwanted reverberations are largely weakened. Experiments were also carried out for two disks of same size by aligning them in the direction of sound emission to quantify shading effect, which shows that shading effect exists locally. Then branching patterns were introduced to the simulation model using L-system that consists of a set of rules to determine how branches grow. The results demonstrates that the simplifications do affect the model accuracy but the influence may be compensated.

Numerous bats species have evolved biosonar to obtain information from their habitats with dense vegetation. Different from man-made sensors, such as stereo cameras and LiDAR, bats' biosonar has much lower spatial resolution and sampling rates. Their biosonar is capable of reliably finding narrow gaps in foliage to serve as a passageway to fly through. To investigate the sensory information under such capability, we have used a biomimetic sonar robot to collect the narrow gap echoes from an artificial hedge in a laboratory setup and from the natural foliage in outdoor environments respectively. The work in this dissertation presents the performance of a conventional energy approach and a deep-learning approach in the classification of echoes from foliage and gap. The deep-learning approach has better foliage versus passageway classification accuracy than the energy approach in both experiments, and it also shows good robustness than the latter one when dealing with data with great varieties in the outdoor experiments. A class activation mapping approach indicates that the initial rising flank inside the echo spectrogram contains critical information. This result corresponds to the neuromorphic spiking model which could be simplified as times where the echo amplitude crosses a certain threshold in a certain frequency range. With these findings, it could be demonstrated that the sensory information in clutter echoes plays an important role in detecting passageways in foliage regardless of the wider beamwith than the passageway geometry. / Doctor of Philosophy / Many bats species are able to navigate and hunt in habitats with dense vegetation based on trains of biosonar echoes as their primary sources for sensory information on the environment. Drones equipped with man-made sensory systems such as optical, thermal, or LiDAR sensors, still face challenges when navigating in dense foliage. Bats are not only able to achieve higher reliability in detecting narrow gaps but accomplish this with much lower spatial resolutions and data rates than those of man-made sensors. To study which sensory information is accessible to bat biosonar for detecting passageways in foliage, a robot consisting of a biomimetic sonar and a camera system has been used to collect a large number of echoes and corresponding images (∼130k samples) from an artificial hedge constructed in the laboratory and various natural foliage targets found outdoors. We have applied a conventional energy approach which is widely used in engineered sonar but is limited by the biosonar's wide beamwidth and only achieves a foliage-versus-passageway classification accuracy of ∼70%. To deal with this situation, a deep-learning approach has been used to improve performance. Besides that, a transparent AI approach has been applied to overcome the black-box property and highlight the region of interest of the deep-learning classifier. The results achieved in detecting passageways were closely matched between the artificial hedge in the laboratory setup and the field data. With the best classification accuracy of 97.13% (artificial hedge) and 96.64% (field data) by the deep-learning approach, this work indicates the potential of exploring sensory information based on clutter echoes from complex environments for detecting passageways in foliage.

Horseshoe bats possess a sophisticated biosonar system that helps them to negotiate complex unstructured environments by relying primarily on the sound as the far sense. For this, the bats emit brief ultrasonic pulses and listen to incoming echoes to learn about the environment. The sites of emission and reception in these bats are surrounded by baffle structures called "noseleaves" and "pinnae (outer ears)". These are the the only places in the biosonar system where direction-dependent information gets encoded. These baffle structures in bats unlike the engineering systems like megaphones have complex static geometry and can undergo fast deformations at the time of pulse emission/reception. However, the functional significance of the baffle motions in biosonar system is not known. The current work primarily focuses on: i) the study of the impact of noseleaf dynamics on the outgoing sound waves, ii) the study of the impact of baffle dynamics on encoding of sensory information and localization performance of bats. For this, we take a numerical approach where we use computer-animated digital models of bat noseleaves that mimic noseleaf dynamics as observed in bats. The shapes are acoustically characterized (beampatterns) numerically using a finite element implementation. These beampatterns are then analyzed using an information-theoretic approach. The followings findings were obtained: i) noseleaf dynamics altered the spatial distribution of energy, ii) baffle dynamics results in encoding of new sensory information, and iii) the new sensory information encoded due to baffle dynamics significantly improves the performance of biosonar system on the two target localization tasks evaluated here -- direction resolution and direction estimation accuracy. These results affirm the importance of dynamics in biosonar system of horseshoe bats and point at the possibility of biosonar dynamics as a key factor behind the astounding sensory capabilities of these animals that are not yet matched by engineering systems. Thus, these biosonar dynamic principles can help improve the man-made sensing systems and help close the performance gap between active sensing in biology and in engineering. / Ph. D.