Example of a tree model created using an L-system.

<p>A) relative position of tree model and simulated sonar beam (sonar position indicated by black dot, -3 dB beamwidth 10°). The leaves with positions within the -3 dB beam contour are colored in black. B) Numerical prediction of impulse response corresponding to the situation depicted in A).</p

Acoustic signals used in the experiments.

<p>A) Example realization of an MLS sequence of length 255. B) Spectrum of an echo triggered by an MLS pulse that was aimed at a cardboard disc (10 cm diameter).</p

Experimental characterization of shading effects between leaves on target strength.

<p>Circles: no shading, crosses: complete shading, squares: partially shaded. The diameters of two disks were 3.6 cm. 50 echoes were collected for each situation, the markers represent the mean and the error bars represent the 75th and 25th percentile of the data set. All amplitude values were normalized with the mean of the impulse response maximums when there was no shading. The inset shows the envelopes of the impulse responses from two discs (the back disc was fully shaded) with a spacing of 25 cm, which indicates that the impulse response from each disc can be distinguished by time.</p

Biomimetic detection of dynamic signatures in foliage echoes

Certain bat species (family Rhinolophidae) dynamically deform their emission baffles (noseleaves) and reception baffles (pinnae) during echolocation. Prior research using numerical models, laboratory characterizations, and experiments with simple targets have suggested that this dynamics may manifest itself in time-variant echo signatures. Since the pronounced random nature of echoes from natural targets such as foliage has not been reflected in these experiments, we have collected a large number (>55,000) of foliage echoes outdoors with a sonar head that mimics the dynamic periphery in bats. The echo data was processed with a custom auditory processing model to create spike-based echo representations. Deep-learning classifiers were able to estimate the dynamic state of the periphery, i.e., static or dynamic, based on single echoes with accuracies of up to 80%. This suggests that the effects of the peripheral dynamics are present in the bat brains and could hence be used by the animals. The best classification performances were obtained for data that was obtained within a spatially confined area. Hence, if the bat brains suffer from the same generalization issues, they would have to have a way to adapt their neural echo processing to such local fluctuations to exploit the dynamic effects successfully

Tree species with their respective estimated equivalent leaf radii used in the acoustic leaf characterizations.

<p>Tree species with their respective estimated equivalent leaf radii used in the acoustic leaf characterizations.</p

Echo envelope inhomogeneity for the L-system tree models compared to a uniform-distribution model reference.

<p>Solid lines: L-system tree models, dashed lines: uniform-distribution model. A) and D) Straight approach towards the foliage (A pine, B ginkgo); B) and E) Angular scan with viewing angles ranging from 90° to 0° (oriented straight at the center, B) pine, E) ginkgo); C) and F) Change in -3 dB beamwidth (C pine, F ginkgo). The leaf density of the uniformly distributed reference models was adjusted in each condition to match the number of leaves in the sonar beam of the two L-system models. In addition, the size of the leaf domain in the uniform leaf distribution model was adjusted to match the echo length in L-systems. Each point represents the mean of 100 experiments, the error bars indicate the minimum and maximum values in each data set. The insets in each panel show an example waveform with a parameter value in the middle of the range shown in the main figure. Inhomogeneity is the root mean square of the difference between the two means (mean of original envelope and mean of permuted envelope) across 100 windows.</p

Leaf target strength as a function of equivalent radius.

<p>A) Measured values of leaf target strength (maximum impulse response amplitude) from 100 leaf samples of leatherleaf arrowwood (<i>Viburnum rhytidophyllum</i>) together with the prediction from the disk model (solid lines). The measurements were repeated three times and each repetition is indicated by a different marker: filled circle (first repetition); diamond (second repetition); plus sign (third repetition); simulation: solid line. 50 echoes were collected for each leaf in each measurement. Each symbol in the plot is the median of 50 impulse response maximums. The fit of the model was accomplished by picking the value of a scalar scaling factor that minimized the deviations between data and model in a least-square sense. B) Measured values of leaf target strength of broad leaves across 10 species are shown together with the predictions from the disc-model (solid line, model fitting as described in panel A). The silhouettes of the leaves measured are shown in the top of the respective data points. The measurement of ginkgo leaf was conducted with a different sampling frequency (250 kHz); all other conditions were identical. The insets in the panels describe envelopes of example impulse responses in the measurement of individual leaves.</p

Tree species with their respective estimated equivalent leaf radii used in the acoustic leaf characterizations.

<p>Tree species with their respective estimated equivalent leaf radii used in the acoustic leaf characterizations.</p

Tree specimens with their respective digital tree models constructed using L-systems.

<p>A) Eastern white pine (<i>Pinus strobus</i>); B) L system model of the same species [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0189824#pone.0189824.ref015" target="_blank">15</a>] with its 180° rotated version superpositioned to increase the branch density; C) same as B) with leaves added. D),E) and F): same with A), B) and C) for a young ginkgo (<i>Ginkgo biloba</i>) except for 90°’s rotation and that the rotated version is lifted half length of the initial branch along <i>z</i> axis.</p

Distribution of the position of the maximum displacement amplitudes within the pulse duration.

<p>a) position of the maximum displacement within the pulse as a function of pulse length, b) histogram of the position of the maximum displacement within each pulse in percent of pulse duration. In a), different marker shapes denote different individuals and different gray levels of the marker faces (including black and white) signify different pulse sequences. The dashed line in a) marks the pulse duration, i.e., all the points falling below this line indicates that the maximum displacement never occurred after the end of the pulse.</p