The Ecological Conditions That Favor Tool Use and Innovation in Wild Bottlenose Dolphins (Tursiops sp.)

Dolphins are well known for their exquisite echolocation abilities, which enable them to detect and discriminate prey species and even locate buried prey. While these skills are widely used during foraging, some dolphins use tools to locate and extract prey. In the only known case of tool use in free-ranging cetaceans, a subset of bottlenose dolphins (Tursiops sp.) in Shark Bay, Western Australia habitually employs marine basket sponge tools to locate and ferret prey from the seafloor. While it is clear that sponges protect dolphins' rostra while searching for prey, it is still not known why dolphins probe the substrate at all instead of merely echolocating for buried prey as documented at other sites. By ‘sponge foraging’ ourselves, we show that these dolphins target prey that both lack swimbladders and burrow in a rubble-littered substrate. Delphinid echolocation and vision are critical for hunting but less effective on such prey. Consequently, if dolphins are to access this burrowing, swimbladderless prey, they must probe the seafloor and in turn benefit from using protective sponges. We suggest that these tools have allowed sponge foraging dolphins to exploit an empty niche inaccessible to their non-tool-using counterparts. Our study identifies the underlying ecological basis of dolphin tool use and strengthens our understanding of the conditions that favor tool use and innovation in the wild

Neural networks for improved target differentiation and localization with sonar

Cataloged from PDF version of article.This study investigates the processing of sonar signals using neural networks for robust differentiation of commonly encountered features in indoor robot environments. Differentiation of such features is of interest for intelligent systems in a variety of applications. Different representations of amplitude and time-of-¯ight measurement patterns acquired from a real sonar system are processed. In most cases, best results are obtained with the low-frequency component of the discrete wavelet transform of these patterns. Modular and non-modular neural network structures trained with the back-propagation and generating±shrinking algorithms are used to incorporate learning in the identi®cation of parameter relations for target primitives. Networks trained with the generating±shrinking algorithm demonstrate better generalization and interpolation capability and faster convergence rate. Neural networks can differentiate more targets employing only a single sensor node, with a higher correct differentiation percentage (99%) than achieved with previously reported methods (61±90%) employing multiple sensor nodes. A sensor node is a pair of transducers with ®xed separation, that can rotate and scan the target to collect data. Had the number of sensing nodes been reduced in the other methods, their performance would have been even worse. The success of the neural network approach shows that the sonar signals do contain suf®cient information to differentiate all target types, but the previously reported methods are unable to resolve this identifying information. This work can ®nd application in areas where recognition of patterns hidden in sonar signals is required. Some examples are system control based on acoustic signal detection and identi®cation, map building, navigation, obstacle avoidance, and target-tracking applications for mobile robots and other intelligent systems. q 2001 Elsevier Science Ltd. All rights reserved

Radius of curvature estimation and localization of targets using multiple sonar sensors

Acoustic sensors have been widely used in time-of-flight ranging systems since they are inexpensive and convenient to use. One of the most important limitations of these sensors is their low angular resolution. To improve the angular resolution and the accuracy, a novel, flexible, and adaptive three- dimensional (3-D) multi-sensor sonar system is described for estimating the radius of curvature and location of cylindrical and spherical targets. Point, line, and planar targets are included as limiting cases which are important for the characterization of typical environments. Sensitivity analysis of the curvature estimate with respect to measurement errors and certain system parameters is provided. The analysis and the simulations are verified by experiments in 2-D with specularly reflecting cylindrical and planar targets, using a real sonar system. Typical accuracies in range and azimuth are 0.18 mm and 0.1°, respectively. Accuracy of the curvature estimation depends on the target type and system parameters such as transducer separation and operating range. The adaptive configuration brings an improvement varying between 35% and 45% in the accuracy of the curvature estimate. The presented results are useful for target differentiation and tracking applications.A flexible and adaptive three-dimensional multisensor sonar system capable of estimating the location and radius of curvature of spherical and cylindrical targets is presented. The performance radius of curvature estimation is analyzed to provide information for differentiating reflectors with different radii. Results showed that the adaptive configuration improved the accuracy of the curvature estimate between 35% and 45%

A Biologically Inspired Model of Bat Echolocation In A Cluttered Environment With Inputs Designed From Field Recordings

Thesis advisor: Willie J. PadillaThesis advisor: David C. MountainBat echolocation strategies and neural processing of acoustic information, with a focus on cluttered environments, is investigated in this study. How a bat processes the dense field of echoes received while navigating and foraging in the dark is not well understood. While several models have been developed to describe the mechanisms behind bat echolocation, most are based in mathematics rather than biology, and focus on either peripheral or neural processing--not exploring how these two levels of processing are vitally connected. Current echolocation models also do not use habitat specific acoustic input, or account for field observations of echolocation strategies. Here, a new approach to echolocation modeling is described capturing the full picture of echolocation from signal generation to a neural picture of the acoustic scene. A biologically inspired echolocation model is developed using field research measurements of the interpulse interval timing used by a frequency modulating (FM) bat in the wild, with a whole method approach to modeling echolocation including habitat specific acoustic inputs, a biologically accurate peripheral model of sound processing by the outer, middle, and inner ear, and finally a neural model incorporating established auditory pathways and neuron types with echolocation adaptations. Field recordings analyzed underscore bat sonar design differences observed in the laboratory and wild, and suggest a correlation between interpulse interval groupings and increased clutter. The scenario model provides habitat and behavior specific echoes and is a useful tool for both modeling and behavioral studies, and the peripheral and neural model show that spike-time information and echolocation specific neuron types can produce target localization in the midbrain.Thesis (PhD) — Boston College, 2014.Submitted to: Boston College. Graduate School of Arts and Sciences.Discipline: Physics

Amplitude and phase sonar calibration and the use of target phase for enhanced acoustic target characterisation

This thesis investigates the incorporation of target phase into sonar signal processing, for enhanced information in the context of acoustical oceanography. A sonar system phase calibration method, which includes both the amplitude and phase response is proposed. The technique is an extension of the widespread standard-target sonar calibration method, based on the use of metallic spheres as standard targets. Frequency domain data processing is used, with target phase measured as a phase angle difference between two frequency components. This approach minimizes the impact of range uncertainties in the calibration process. Calibration accuracy is examined by comparison to theoretical full-wave modal solutions. The system complex response is obtained for an operating frequency of 50 to 150 kHz, and sources of ambiguity are examined. The calibrated broadband sonar system is then used to study the complex scattering of objects important for the modelling of marine organism echoes, such as elastic spheres, fluid-filled shells, cylinders and prolate spheroids. Underlying echo formation mechanisms and their interaction are explored. Phase-sensitive sonar systems could be important for the acquisition of increased levels of information, crucial for the development of automated species identification. Studies of sonar system phase calibration and complex scattering from fundamental shapes are necessary in order to incorporate this type of fully-coherent processing into scientific acoustic instruments

A comparison of different approaches to target differentiation with sonar

Ankara : The Department of Electrical and Electronics Engineering and the Institute of Engineering and Science of Bilkent University, 2001.Thesis (Ph.D.) -- Bilkent University, 2001.Includes bibliographical references leaves 180-197This study compares the performances of di erent classication schemes and fusion techniques for target di erentiation and localization of commonly encountered features in indoor robot environments using sonar sensing Di erentiation of such features is of interest for intelligent systems in a variety of applications such as system control based on acoustic signal detection and identication map building navigation obstacle avoidance and target tracking The classication schemes employed include the target di erentiation algorithm developed by Ayrulu and Barshan statistical pattern recognition techniques fuzzy c means clustering algorithm and articial neural networks The fusion techniques used are Dempster Shafer evidential reasoning and di erent voting schemes To solve the consistency problem arising in simple ma jority voting di erent voting schemes including preference ordering and reliability measures are proposed and veried experimentally To improve the performance of neural network classiers di erent input signal representations two di erent training algorithms and both modular and non modular network structures are considered The best classication and localization scheme is found to be the neural network classier trained with the wavelet transform of the sonar signals This method is applied to map building in mobile robot environments Physically di erent sensors such as infrared sensors and structured light systems besides sonar sensors are also considered to improve the performance in target classication and localization.Ayrulu (Erdem), BirselPh.D