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Abstract Background Computer Aided Lung Sound Analysis (CALSA) aims to overcome limitations associated with standard lung auscultation by removing the subjective component and allowing quantification of sound characteristics. In this proof-of-concept study, a novel automated approach was evaluated in real patient data by comparing lung sound characteristics to structural and functional imaging biomarkers. Methods Patients with cystic fibrosis (CF) aged > 5y were recruited in a prospective cross-sectional study. CT scans were analyzed by the CF-CT scoring method and Functional Respiratory Imaging (FRI). A digital stethoscope was used to record lung sounds at six chest locations. Following sound characteristics were determined: expiration-to-inspiration (E/I) signal power ratios within different frequency ranges, number of crackles per respiratory phase and wheeze parameters. Linear mixed-effects models were computed to relate CALSA parameters to imaging biomarkers on a lobar level. Results 222 recordings from 25 CF patients were included. Significant associations were found between E/I ratios and structural abnormalities, of which the ratio between 200 and 400 Hz appeared to be most clinically relevant due to its relation with bronchiectasis, mucus plugging, bronchial wall thickening and air trapping on CT. The number of crackles was also associated with multiple structural abnormalities as well as regional airway resistance determined by FRI. Wheeze parameters were not considered in the statistical analysis, since wheezing was detected in only one recording. Conclusions The present study is the first to investigate associations between auscultatory findings and imaging biomarkers, which are considered the gold standard to evaluate the respiratory system. Despite the exploratory nature of this study, the results showed various meaningful associations that highlight the potential value of automated CALSA as a novel non-invasive outcome measure in future research and clinical practice.

Background: Three-dimensional (3D) printing has become increasingly affordable. Several research projects used 3D printing to create in vitro upper airways model. However, studies using a mainstream desktop 3D printer never performed geometric validation of their model. The aim of this study was to perform geometric validation of a pediatric upper airways model printed with a mainstream desktop 3D printer. Methods: Head computerized tomography (CT) scan of a 10-month-old female underwent segmentation between airways and surrounding anatomical structures. Airways segmentation allowed their measurement for further comparison with printed model. Head segmentation enabled the creation of a 3D printable volume file. To proceed to the geometric validation of the head model, the latter underwent a CT scan. Similar segmentation work was performed on the printed model for comparison. Overlap proportion between the original infant volume and the printed model as well as an average Hausdorff distance were calculated after manual alignment between the original and printed model. Results: Volumes were 12.31 cm3 and 12.32 cm3 for the pediatric and the printed model upper airways, respectively (0.08% difference). Dice coefficient of original and printed model was 0.92. The average Hausdorff distance was 0.21 mm. Conclusion: Desktop mainstream 3D printers can generate pediatric upper airway model with a high dimensional accuracy, as evidenced by our comprehensive geometrical validation.

Airborne 3D imaging using ultrasound is a promising sensing modality for robotic applications in harsh environments. Over the last decade, several high-performance systems have been proposed in the literature. Most of these sensors use a reduced aperture microphone array, leading to artifacts in the resulting acoustic images. This paper presents a novel in-air ultrasound sensor that incorporates 1024 microphones, in a 32-by-32 uniform rectangular array, in combination with a distributed embedded hardware design to perform the data acquisition. Using a broadband Minimum Variance Distortionless Response (MVDR) beamformer with Forward-Backward Spatial Smoothing (FB-SS), the sensor is able to create both 2D and 3D ultrasound images of the full-frontal hemisphere with high angular accuracy with up to 70 $\mathrm { \text {dB}}$ main lobe to side lobe ratioin the array response in a single-source scenario. This paper describes both the hardware infrastructure needed to obtain such highly detailed acoustical images, as well as the signal processing chain needed to convert the raw acoustic data into said images. Utilizing this novel high-resolution ultrasound imaging sensor, we wish to investigate the limits of both passive and active airborne ultrasound sensing by utilizing this virtually artifact-free imaging modality.

Echolocation is the prime sensing modality for many species of bats, who show the intricate ability to perform a plethora of tasks in complex and unstructured environments. Understanding this exceptional feat of sensorimotor interaction is a key aspect into building more robust and performant man-made sonar sensors. In order to better understand the underlying perception mechanisms it is important to get a good insight into the nature of the reflected signals that the bat perceives. While ensonification experiments are an important way to better understand the nature of these signals, they are as time-consuming to perform as they are informative. In this paper we present SonoTraceLab, an open-source software package for simulating both technical as well as biological sonar systems in complex scenes. Using simulation approaches can drastically increase insights into the nature of biological echolocation systems, while reducing the time- and material complexity of performing them.

Automated analysis of lung sounds is a non-invasive technique that has the potential to become a powerful tool for the detection of respiratory illnesses. In this paper, we propose a machine learning method tasked to predict the number of respiratory crackles in a recording. More specifically, a Convolutional Neural Network (CNN) regression algorithm with a spectrogram input. Firstly, a large set of simulated data is used to train four different CNN designs. We use a lung sound simulator because of the absence of a large public database with sufficiently labelled lung recordings. When comparing the performance of these designs on simulated sounds, we concluded that maximum-pooling performed better than stride in the convolution layers. Furthermore, multiple consecutive convolution layers with small kernels outperformed single convolution layers using large kernels with the same receptive field. The best performing CNN achieved a root mean square error of 6.26 crackles per recording with an average of 50 crackles per recording. Secondly, we validate the best CNN design on a set of 51 real respiratory recordings that are labelled with a perceived degree of crackles: no crackles, mild, moderate, and severe. The system is capable of detecting an increasing amount of crackles in the respective last three categories. These results show the effectiveness of this novel approach to use simulated data for the development of lung sound analysis algorithms.

The predictive brain hypothesis suggests that perception can be interpreted as the process of minimizing the error between predicted perception tokens generated via an internal world model and actual sensory input tokens. When implementing working examples of this hypothesis in the context of in-air sonar, significant difficulties arise due to the sparse nature of the reflection model that governs ultrasonic sensing. Despite these challenges, creating consistent world models using sonar data is crucial for implementing predictive processing of ultrasound data in robotics. In an effort to enable robust robot behavior using ultrasound as the sole exteroceptive sensor modality, this paper introduces EchoPT (Echo-Predicting Pretrained Transformer), a pretrained transformer architecture designed to predict 2D sonar images from previous sensory data and robot ego-motion information. We detail the transformer architecture that drives EchoPT and compare the performance of our model to several state-of-the-art techniques. In addition to presenting and evaluating our EchoPT model, we demonstrate the effectiveness of this predictive perception approach in two robotic tasks.

As autonomous platforms become more advanced, it is useful to obtain more information about the environment. Classic vision sensors such as RGB-D cameras, LiDAR, or acoustical imaging cameras are capable of accurately displaying the location of an object in 3D space and are widely used for this purpose. However, a problem arises when the measurement environment becomes more complex and measurement data might begin to suffer in terms of accuracy. Imaging sonar sensors are developed specifically for these environments but are limited in terms of frame rate due to the inherent slow nature of sound. This paper will propose a method to increase the amount of useful information obtained from the available data from a single measurement in post-processing. Utilizing a signal in combination with a Doppler velocity-tuned matched-filter bank it is possible to estimate the radial velocity of an object with respect to the sensor’s location. This allows the robot to make better decisions when it comes to path planning and collision avoidance, as a rapidly approaching object requires a different action than a stationary object or one moving away from the sensor. Another advantage of this system is that a single seemingly-large object might be identified as two close lying objects with different speeds. This paper serves as a proof-of-concept with results from a realistic simulation environment using an imaging sonar sensor and shows that the proposed method is perfectly suitable for making accurate estimations of an object’s radial velocity.

In this paper, we introduce SonoNERFs, a novel approach that adapts Neural Radiance Fields (NeRFs) to model and understand the echolocation process in bats, focusing on the challenges posed by acoustic data interpretation without phase information. Leveraging insights from the field of optical NeRFs, our model, termed SonoNERF, represents the acoustic environment through Neural Reflectivity Fields. This model allows us to reconstruct three-dimensional scenes from echolocation data, obtained by simulating how bats perceive their surroundings through sound. By integrating concepts from biological echolocation and modern computational models, we demonstrate the SonoNERF’s ability to predict echo spectrograms for unseen echolocation poses and effectively reconstruct a mesh-based and energy-based representation of complex scenes. Our work bridges a gap in understanding biological echolocation and proposes a methodological framework that provides a first-order model of how scene understanding might arise in echolocating animals. We demonstrate the efficacy of the SonoNERF model on three scenes of increasing complexity, including some biologically relevant prey–predator interactions.

Abstract Background Gravity, and thus body position, can affect the regional distribution of lung ventilation and blood flow. Therefore, body positioning is a potential tool to improve regional ventilation, thereby possibly enhancing the effect of respiratory physiotherapy interventions. In this proof-of-concept study, functional respiratory imaging (FRI) was used to objectively assess effects of body position on regional airflow distribution in the lungs. Methods Five healthy volunteers were recruited. The participants were asked during FRI first to lie in supine position, afterwards in standardized right lateral position. Results In right lateral position there was significantly more regional ventilation also described as Imaging Airflow Distribution in the right lung than in the left lung (P

High-performance sensing and control systems have an important role in Industry 4.0. However, with the current solutions, the development effort is high and requires specialized skills in electronic engineering. Therefore, a model-based approach on control and signal processing systems using affordable heterogeneous hardware is proposed. In this work, a model-based code generator is developed to abstract the user from the actual software implementation. Starting from a combined model of a timing diagram and an embedded platform, a model transformation is used to automatically generate functional acquisition firmware. This firmware generator enables system engineers without deep software and hardware knowledge to set up complex control systems. Furthermore, it equips software engineers with a solid framework for faster development.

Verreycken and colleagues present a methods paper detailing construction of large, scalable microphone arrays for bio-acoustic monitoring of animal species. They demonstrate the efficacy of their method in detailing bat hunting echolocation beams with a 64-microphone array, and localizing songbirds across a larger habitat.

Sensors using ultrasonic sound have proven to provide accurate 3D perception in difficult environments where other modalities fail. Several industrial sectors need accurate and reliable sensing in these harsh conditions. The conventional LiDAR/camera approach in many state-of-the-art autonomous navigation methods is limited to environments with optimal sensing conditions for visual modalities. The use of other sensing modalities can thus improve reliability and usability and increase the application potential of autonomous agents. Ultrasonic measurements provide, compared to LiDAR, a much sparser representation of the environment, making a direct replacement of the LiDAR sensor difficult. In this work, we propose a method to predict LiDAR point cloud data from an in-air acoustic sonar sensor using a convolutional stacked autoencoder. This provides a robotic system with high-resolution measurements and allows for easier integration into existing systems to safely navigate environments where visual modalities become unreliable and less accurate. A video of our predictions is available at https://youtu.be/jlx1S-tslmo.

During the last decade, the concept of smart cities is gaining popularity, and many cities all around the globe are adopting it. Lately, the Smart Pedestrian Crossing (SPC) idea has become an important part of the smart city concept. The main objective of the SPC is to achieve a safe and smooth traffic flow of pedestrians and vehicles in the smart city. In this paper, we present a simple yet efficient localization system which is suitable for locating people over a pedestrian crossing. By adopting the proposed localization system, the people flow over the pedestrian crossing can be observed. Consequently, a safe and efficient traffic flow in the smart city can be achieved. The proposed system utilizes Internet of Things (IoT) transceivers that are carried by the pedestrians or embedded in the pedestrians' smart devices. These transceivers can be activated to transmit a signal every few seconds when the pedestrian passes over a pedestrian crossing. These periodic signals will be received by a single Angle of Arrival (AoA) estimation system to estimate the direction of the pedestrian. Furthermore, the pedestrian direction and the environmental features of the pedestrian crossings are utilized in a 1D particle filter to estimate accurately the pedestrian's location. The proposed system has been validated experimentally in the smart zone of Antwerp city. The experimental results reveal that the proposed localization system can provide an accurate, cost effective and reliable localization solution to this specific problem without violating the pedestrian privacy.

Bat-pollinated flowers have to attract their pollinators in absence of light and therefore some species developed specialized echoic floral parts. These parts are usually concave shaped and act like acoustic retroreflectors making the flowers acoustically conspicuous to the bats. Acoustic plant specializations only have been described for two bat-pollinated species in the Neotropics and one other bat-dependent plant in South East Asia. However, it remains unclear whether other bat-pollinated plant species also show acoustic adaptations. Moreover, acoustic traits have never been compared between bat-pollinated flowers and flowers belonging to other pollination syndromes. To investigate acoustic traits of bat-pollinated flowers we recorded a dataset of 32320 flower echoes, collected from 168 individual flowers belonging to 12 different species. 6 of these species were pollinated by bats and 6 species were pollinated by insects or hummingbirds. We analyzed the spectral target strength of the flowers and trained a convolutional neural network (CNN) on the spectrograms of the flower echoes. We found that bat-pollinated flowers have a significantly higher echo target strength, independent of their size, and differ in their morphology, specifically in the lower variance of their morphological features. We found that a good classification accuracy by our CNN (up to 84%) can be achieved with only one echo/spectrogram to classify the 12 different plant species, both bat-pollinated and otherwise, with bat-pollinated flowers being easier to classify. The higher classification performance of bat-pollinated flowers can be explained by the lower variance of their morphology.

In a standard state-of-the-art measurement the head-related transfer function (HRTF) is obtained in an anechoic room with an elaborate setup involving multiple calibrated loudspeakers. In search for a simplified method that would open up the possibility for an HRTF measurement in a home environment, it has been suggested that this setup could be replaced with one with a single, fixed loudspeaker. In such a setup, the subject samples different directions by moving the head with respect to this loudspeaker, while the head movements are tracked in some way. In this paper, the feasibility of such an approach is studied. To this end, the HRTF is measured in an unmodified (non-anechoic) room by means of a single external speaker and a high resolution head tracking system. The differences between the dynamically obtained HRTF and the standard static HRTF are investigated, and are shown to be mostly due to variable torso reflections.

In this paper, we present two localization algorithms that exploit the Angle of Arrival (AoA) parameters of the received signal. The proposed ANGular Location Estimation (ANGLE) algorithms utilize a probabilistic model to describe the angular response of the received signal. Consequently, the ANGLE algorithms can estimate the location of a transmitter using a single step Hadamard product. The first algorithm utilizes a Single Sample of the received signal (ANGLE-SS). The second algorithm, on the other hand, employs the signal Subspace Decomposition technique (ANGLE-SD). The localization capabilities of the ANGLE algorithms have been experimentally investigated in an office environment. The performances of the ANGLE algorithms have been validated against the performances of several AoA-based localization systems. The experimental results show that the ANGLE-SD algorithm outperforms all the studied AoA-based localization systems. The ANGLE-SS algorithm, on the other hand, outperforms every localization system that utilizes less than 50 samples of the received signal. The ANGLE algorithms are flexible, generic and computationally very efficient. These features allow the ANGLE algorithms to be easily deployed in any existing AoA-based localization system.

State-of-the-art autonomous vehicles use all kinds of sensors based on light, such as a camera or LIDAR (Laser Imaging Detection And Ranging). These sensors tend to fail when exposed to airborne particles. Ultrasonic sensors have the ability to work in these environments since they have longer wavelengths and are based on acoustics, making them able to pass through the mentioned distortions. However, they have a lower angular resolution compared to their optical counterparts. In this article a 3D in-air sonar sensor is simulated, consisting of a Uniform Rectangular Array similar to the newly developed micro Real Time Imaging Sonar (μRTIS) by CoSys-Lab. Different direction of arrival techniques will be compared for an 8 by 8 uniform rectangular microphone array in a simulation environment to investigate the influence of different parameters in a completely controlled environment. We will investigate the influence of the signal-to-noise ratio and number of snapshots to the angular and spatial resolution in the direction parallel and perpendicular to the direction of the emitted signal, respectively called the angular and range resolution. We will compare these results with real-life imaging results of the μRTIS. The results presented in this article show that, despite the fact that in-air sonar applications are limited to only one snapshot, more advanced algorithms than Delay-And-Sum beamforming are viable options, which is confirmed with the real-life data captured by the μRTIS.

In this paper, a model-based firmware generator is presented towards complex sampling schemes. The framework is capable of automatically generating a fixed-rate Shannon-compliant acquisition scheme, as well as a variable-rate compressive sensing acquisition scheme. The generation starts from a model definition, which consists of two main components, namely an acquisition sequence to implement and the platform on which the sequence should be implemented. This model is then combined with the specifications to be transformed into a functional firmware. When generating firmware for compressive sensing (CS) purposes, the defined acquisition sequence is automatically generated to implement a pseudo-random sampling scheme in agreement with the defined undersampling factor. The evaluation of the generated firmware is done by means of an example use-case, including a proposed strategy for synchronization between CS setups. This research attempts to reduce the development complexity for embedded CS to lower the threshold towards effective usage in the field.

In this paper, a novel human body pose estimation system is introduced, which features a synchronous network of wirelessly connected sensor nodes, thus forming a wireless body sensor network (WBSN). This measurement architecture for establishing human body pose estimations works in combination with the HTC Vive base stations and a Wi-Fi router. The former emits infrared pulses and laser sweeps in the horizontal and vertical plane captured by the sensor nodes' photodiodes. The infrared data can be converted into an azimuth and elevation angle for each photodiode relative to the emitting HTC Vive device. To estimate the human body poses, the sensor data are fed into a probabilistic non-linear maximum likelihood estimator combined with a parametric human body model. Using a parametric human body model for estimating the poses proves to be more resilient to sensor noise compared to estimating the Six Degrees of Freedom (6DoF) of every sensor node individually. The solution space can be constrained to the parameters of the users body model that relates to a priori information on the subject. The combination of the proposed hardware sensor network, its synchronous sensor data, and processing algorithms yields a cost-efficient human body pose estimation system.

Navigation in varied and dynamic indoor environments remains a complex task for autonomous mobile platforms. Especially when conditions worsen, typical sensor modalities may fail to operate optimally and subsequently provide inapt input for safe navigation control. In this study, we present an approach for the navigation of a dynamic indoor environment with a mobile platform with a single or several sonar sensors using a layered control system. These sensors can operate in conditions such as rain, fog, dust, or dirt. The different control layers, such as collision avoidance and corridor following behavior, are activated based on acoustic flow queues in the fusion of the sonar images. The novelty of this work is allowing these sensors to be freely positioned on the mobile platform and providing the framework for designing the optimal navigational outcome based on a zoning system around the mobile platform. Presented in this paper is the acoustic flow model used, as well as the design of the layered controller. Next to validation in simulation, an implementation is presented and validated in a real office environment using a real mobile platform with one, two, or three sonar sensors in real time with 2D navigation. Multiple sensor layouts were validated in both the simulation and real experiments to demonstrate that the modular approach for the controller and sensor fusion works optimally. The results of this work show stable and safe navigation of indoor environments with dynamic objects.

The bearings of rotating machinery often fail, leading to unforeseen downtime of large machines in industrial plants. Therefore, condition monitoring can be a powerful tool to aid in the quick identification of these faults and make it possible to plan maintenance before the fault becomes too drastic, reducing downtime and cost. Predictive maintenance is often based on information gathered from accelerometers. However, these sensors are contact-based, making them less attractive for use in an industrial plant and more prone to breakage. In this paper, condition monitoring based on ultrasound is researched, where non-invasive sensors are used to record the noise originating from different defects of the Machinery Fault Simulator. The acoustic data are recorded using a sparse microphone array in a lab environment. The same array was used to record real spatial noise in a fully operational plant which was later added to the acoustic data containing the different defects with a variety of Signal To Noise ratios. In this paper, we compare the classification results of the noisy acoustic data of only one microphone to the beamformed acoustic data. We do this to investigate how beamforming could improve the classification process in an ultrasound condition-monitoring application in a real industrial plant.

Most objects and vegetation making up the habitats of echolocating bats return a multitude of overlapping echoes. Recent evidence suggests that the limited temporal and spatial resolution of bio-sonar prevents bats from separately perceiving the objects giving rise to these overlapping echoes. Therefore, bats often operate under conditions where their ability to localize obstacles is severely limited. Nevertheless, bats excel at avoiding complex obstacles. In this paper, we present a robotic model of bat obstacle avoidance using interaural level differences and distance to the nearest obstacle as the minimal set of cues. In contrast to previous robotic models of bats, the current robot does not attempt to localize obstacles. We evaluate two obstacle avoidance strategies. First, the Fixed Head Strategy keeps the acoustic gaze direction aligned with the direction of flight. Second, the Delayed Linear Adaptive Law (DLAL) Strategy uses acoustic gaze scanning, as observed in hunting bats. Acoustic gaze scanning has been suggested to aid the bat in hunting for prey. Here, we evaluate its adaptive value for obstacle avoidance when obstacles can not be localized. The robot's obstacle avoidance performance is assessed in two environments mimicking (highly cluttered) experimental setups commonly used in behavioral experiments: a rectangular arena containing multiple complex cylindrical reflecting surfaces and a corridor lined with complex reflecting surfaces. The results indicate that distance to the nearest object and interaural level differences allows steering the robot clear of obstacles in environments that return non-localizable echoes. Furthermore, we found that using acoustic gaze scanning reduced performance, suggesting that gaze scanning might not be beneficial under conditions where the animal has limited access to angular information, which is in line with behavioral evidence.

Pulse-echo sensing is the driving principle behind biological echolocation as well as biologically-inspired sonar and radar sensors. In biological echolocation, a single emitter sends a self-generated pulse into the environment which reflects off objects. A fraction of these reflections are captured by two receivers as echoes, from which information about the objects, such as their position in 3D space, can be deduced by means of timing, intensity and spectral analysis. This is opposed to frequency-modulated continuous-wave radar, which analyses the shift in frequency of the returning signal to determine distance, and requires an array of antenna to obtain directional information. In this work, we present a novel simulator which can generate synthetic pulse-echo measurements for a simulated sensor in a virtual environment. The simulation is implemented by replicating the relevant physical processes underlying the pulse-echo sensing modality, while achieving high performance at update rates above 50 Hz. The system is built to perform design space exploration of sensor hardware and software, with the goals of rapid prototyping and preliminary safety testing in mind. We demonstrate the validity of the simulator by replicating real-world experiments from previous work. In the first case, a subsumption architecture vehicle controller is set to navigate an unknown environment using the virtual sensor. We see the same trajectory pattern emerge in the simulated environment rebuilt from the real experiment, as well as similar activation times for the high-priority behaviors (±1.9%), and low-priority behaviors (±0.2%). In a second experiment, the simulated signals are used as input to a biologically-inspired direct simultaneous mapping and localization (SLAM) algorithm. Using only path integration, 83% of the positional errors are larger than 10 m, while for the SLAM algorithm 95% of the errors are smaller than 3.2 m. Additionally, we perform design space exploration using the simulator. By creating a synthetic radiation pattern with increased spatiospectral variance, we are able to reduce the average localization error of the system by 11%. From these results, we conclude that the simulation is sufficiently accurate to be of use in developing vehicle controllers and SLAM algorithms for pulse-echo radar sensors.

Echolocating bats have excellent spatial memory and are able to navigate to salient locations using bio-sonar. Navigating and route-following require animals to recognize places. Currently, it is mostly unknown how bats recognize places using echolocation. In this paper, we propose template based place recognition might underlie sonar-based navigation in bats. Under this hypothesis, bats recognize places by remembering their echo signature - rather than their 3D layout. Using a large body of ensonification data collected in three different habitats, we test the viability of this hypothesis assessing two critical properties of the proposed echo signatures: (1) they can be uniquely classified and (2) they vary continuously across space. Based on the results presented, we conclude that the proposed echo signatures satisfy both criteria. We discuss how these two properties of the echo signatures can support navigation and building a cognitive map.

We propose to combine a biomimetic navigation model which solves a simultaneous localization and mapping task with a biomimetic sonar mounted on a mobile robot to address two related questions. First, can robotic sonar sensing lead to intelligent interactions with complex environments? Second, can we model sonar based spatial orientation and the construction of spatial maps by bats? To address these questions we adapt the mapping module of RatSLAM, a previously published navigation system based on computational models of the rodent hippocampus. We analyze the performance of the proposed robotic implementation operating in the real world. We conclude that the biomimetic navigation model operating on the information from the biomimetic sonar allows an autonomous agent to map unmodified (office) environments efficiently and consistently. Furthermore, these results also show that successful navigation does not require the readings of the biomimetic sonar to be interpreted in terms of individual objects/landmarks in the environment. We argue that the system has applications in robotics as well as in the field of biology as a simple, first order, model for sonar based spatial orientation and map building.

Bats typically emit multi harmonic calls. Their head morphology shapes the emission and hearing sound fields as a function of frequency. Therefore, the sound fields are markedly different for the various harmonics. As the sound field provides bats with all necessary cues to locate objects in space, different harmonics might provide them with variable amounts of information about the location of objects. Also, the ability to locate objects in different parts of the frontal hemisphere might vary across harmonics. This paper evaluates this hypothesis in R. rouxi, using an information theoretic framework. We estimate the reflector position information transfer in the echolocation system of R. rouxi as a function of frequency. This analysis shows that localization performance reaches a global minimum and a global maximum at the two most energetic frequency components of R. rouxi call indicating tuning of morphology and harmonic structure. Using the fundamental the bat is able to locate objects in a large portion of the frontal hemisphere. In contrast, using the 1st overtone, it can only locate objects, albeit with a slightly higher accuracy, in a small portion of the frontal hemisphere by reducing sensitivity to echoes from outside this region of interest. Hence, different harmonic components provide the bat either with a wide view or a focused view of its environment. We propose these findings can be interpreted in the context of the foraging behaviour of R. rouxi, i.e., hunting in cluttered environments. Indeed, the focused view provided by the 1st overtone suggests that at this frequency its morphology is tuned for clutter rejection and accurate localization in a small region of interest while the finding that overall localization performance is best at the fundamental indicates that the morphology is simultaneously tuned to optimize overall localization performance at this frequency.