Exploring the link between a novel approach for computer aided lung sound analysis and imaging biomarkers: a cross-sectional study.

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.

The effect of posture on airflow distribution, airway geometry and air velocity in healthy subjects.

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 < 0.001). Air velocity was significantly higher in the left lung (P < 0.05). In right lateral position there was significantly more airflow distribution in the right lung than in the left lung (P < 0.001). Significant changes were observed in airway geometry resulting in a decrease in imaged airway volume (P = 0.024) and a higher imaged airway resistance (P = 0.029) in the dependent lung. In general, the effect of right lateral position caused a significant increase in regional ventilation (P < 0.001) in the dependent lung when compared with the supine position. CONCLUSIONS: Changing body position leads to significant changes in regional lung ventilation, objectively assessed by FRI The volume based on the imaging parameters in the dependent lung is smaller in the lateral position than in the supine position. In right lateral decubitus position, airflow distribution is greater in dependent lung compared to the nondependent lung. TRIAL REGISTRATION: The trial has been submitted to www. CLINICALTRIALS: gov with identification number NCT01893697 on 07/02/2013.

Automated Firmware Generation for Compressive Sensing on Heterogeneous Hardware.

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.

Real-Time Sonar Fusion for Layered Navigation Controller.

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.

Comprehensive Analysis System for Automated Respiratory Cycle Segmentation and Crackle Peak Detection.

Digital auscultation is a well-known method for assessing lung sounds, but remains a subjective process in typical practice, relying on the human interpretation. Several methods have been presented for detecting or analyzing crackles but are limited in their real-world application because few have been integrated into comprehensive systems or validated on non-ideal data. This work details a complete signal analysis methodology for analyzing crackles in challenging recordings. The procedure comprises five sequential processing blocks: (1) motion artifact detection, (2) deep learning denoising network, (3) respiratory cycle segmentation, (4) separation of discontinuous adventitious sounds from vesicular sounds, and (5) crackle peak detection. This system uses a collection of new methods and robustness-focused improvements on previous methods to analyze respiratory cycles and crackles therein. To validate the accuracy, the system is tested on a database of 1000 simulated lung sounds with varying levels of motion artifacts, ambient noise, cycle lengths and crackle intensities, in which ground truths are exactly known. The system performs with average F-score of 91.07% for detecting motion artifacts and 94.43% for respiratory cycle extraction, and an overall F-score of 94.08% for detecting the locations of individual crackles. The process also successfully detects healthy recordings. Preliminary validation is also presented on a small set of 20 patient recordings, for which the system performs comparably. These methods provide quantifiable analysis of respiratory sounds to enable clinicians to distinguish between types of crackles, their timing within the respiratory cycle, and the level of occurrence. Crackles are one of the most common abnormal lung sounds, presenting in multiple cardiorespiratory diseases. These features will contribute to a better understanding of disease severity and progression in an objective, simple and non-invasive way.

Acoustic traits of bat-pollinated flowers compared to flowers of other pollination syndromes and their echo-based classification using convolutional neural networks.

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.

Bio-acoustic tracking and localization using heterogeneous, scalable microphone arrays.

Microphone arrays are an essential tool in the field of bioacoustics as they provide a non-intrusive way to study animal vocalizations and monitor their movement and behavior. Microphone arrays can be used for passive localization and tracking of sound sources while analyzing beamforming or spatial filtering of the emitted sound. Studying free roaming animals usually requires setting up equipment over large areas and attaching a tracking device to the animal which may alter their behavior. However, monitoring vocalizing animals through arrays of microphones, spatially distributed over their habitat has the advantage that unrestricted/unmanipulated animals can be observed. Important insights have been achieved through the use of microphone arrays, such as the convergent acoustic field of view in echolocating bats or context-dependent functions of avian duets. Here we show the development and application of large flexible microphone arrays that can be used to localize and track any vocalizing animal and study their bio-acoustic behavior. In a first experiment with hunting pallid bats the acoustic data acquired from a dense array with 64 microphones revealed details of the bats' echolocation beam in previously unseen resolution. We also demonstrate the flexibility of the proposed microphone array system in a second experiment, where we used a different array architecture allowing to simultaneously localize several species of vocalizing songbirds in a radius of 75 m. Our technology makes it possible to do longer measurement campaigns over larger areas studying changing habitats and providing new insights for habitat conservation. The flexible nature of the technology also makes it possible to create dense microphone arrays that can enhance our understanding in various fields of bioacoustics and can help to tackle the analytics of complex behaviors of vocalizing animals.

Beamforming Applied to Ultrasound Analysis in Detection of Bearing Defects.

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.

Simulation of Pulse-Echo Radar for Vehicle Control and SLAM.

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.

An Optimized Spatial Sampling Strategy for Wide-View Planar Array 3-D Sonar Sensors.

Array-based imaging sonar sensors often have the advantage that the sampling directions in which sensor data are processed can be arbitrarily chosen. The associated computational cost of the imaging algorithm scales linearly with the number of sampled directions, though, which may limit the use of such sensors for real-time applications such as robotics. This is especially true in the case of wide-field sensors, which generally require a larger number of sampled directions (e.g., to cover the full frontal hemisphere). For this reason, it is necessary to optimize the set of sampling directions and reduce its number to a minimum. In this article, we propose a spatial sampling strategy for array-based sonar sensors, which considers the point-spread function of the array sensor to obtain such a minimal set. We apply this strategy to optimize the set of sampling directions for a specific planar array sonar sensor and compare the efficiency of the resulting sampling grid with two commonly used sampling strategies.

Bioinspired sonar reflectors as guiding beacons for autonomous navigation.

Sonar sensors are universally applied in autonomous vehicles such as robots and driverless cars as they are inexpensive, energy-efficient, and provide accurate range measurements; however, they have some limitations. Their measurements can lead to ambiguous estimates and echo clutter can hamper target detection. In nature, echolocating bats experience similar problems when searching for food, especially if their food source is close to vegetation, as is the case for gleaning bats and nectar-feeding bats. However, nature has come up with solutions to overcome clutter problems and acoustically guide bats. Several bat-pollinated plants have evolved specially shaped floral parts that act as sonar reflectors, making the plants acoustically conspicuous. Here we show that artificial sonar beacons inspired by floral shapes streamline the navigation efficacy of sonar-guided robot systems. We developed floral-inspired reflector forms and demonstrate their functionality in 2 proof-of-principle experiments. First we show that the reflectors are easily recognized among dense clutter, and second we show that it is possible to discern different reflector shapes and use this identification to guide a robot through an unfamiliar environment. Bioinspired sonar reflectors could have a wide range of applications that could significantly advance sonar-guided systems.

Avoidance of non-localizable obstacles in echolocating bats: A robotic model.

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.

Bats Actively Use Leaves as Specular Reflectors to Detect Acoustically Camouflaged Prey.

Filtering relevant signals from noisy sensory input is a crucial challenge for animals [1, 2]. Many bats are acoustic specialists relying on sound to find prey. They discern salient acoustic signals from irrelevant background masking noise. It has long been considered a sensory impossibility for bats to use solely echolocation for the detection of silent and motionless prey resting directly on foliage due to the masking effects of background echoes [3, 4]. Some bats, however, do successfully perform this seemingly impossible task [5], raising the question-what underlying acoustic and behavioral mechanisms do bats use to solve this conundrum? To address this question, we used biomimetic sonar to record high-resolution measurements of echoes from insects resting on leaves. Based on our echo recordings, we predicted optimal approach angles from which masking echoes can best be avoided. In behavioral experiments, we put these predictions to test. We recorded the prey approach behavior of wild bats in a flight cage equipped with an ultrasonic microphone synchronized with two high-speed cameras for 3D flightpath reconstructions. Bats approached prey from our predicted optimal oblique angles, using the leaf as a specular reflector to uncover previously acoustically hidden prey. Our findings disclose key behavioral and acoustic mechanisms enabling the detection of prey echoes that background clutter would otherwise mask. This work adds to the fundamental understanding of how bat echolocation strategies can override acoustic camouflage by silent, motionless prey, thus providing new insights into the evolutionary arms race between predators and their prey.

Low-cost synchronization of high-speed audio and video recordings in bio-acoustic experiments.

In this paper, we present a method for synchronizing high-speed audio and video recordings of bio-acoustic experiments. By embedding a random signal into the recorded video and audio data, robust synchronization of a diverse set of sensor streams can be performed without the need to keep detailed records. The synchronization can be performed using recording devices without dedicated synchronization inputs. We demonstrate the efficacy of the approach in two sets of experiments: behavioral experiments on different species of echolocating bats and the recordings of field crickets. We present the general operating principle of the synchronization method, discuss its synchronization strength and provide insights into how to construct such a device using off-the-shelf components.

Place recognition using batlike sonar.

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.

Human-computer interface using a head mounted camera and IR markers.

This paper describes an alternative way to control the selection of items in computing devices. A survey of the issues of the current state-of-the art is performed and a solution is presented based on a cheap, head-mounted, IR sensitive camera tracking IR LEDs. Benefits related to this approach are indicated, initial performance results are presented from which we can conclude the feasibility of the proposed solution.

Drivers for the renaissance of coal.

Coal was central to the industrial revolution, but in the 20th century it increasingly was superseded by oil and gas. However, in recent years coal again has become the predominant source of global carbon emissions. We show that this trend of rapidly increasing coal-based emissions is not restricted to a few individual countries such as China. Rather, we are witnessing a global renaissance of coal majorly driven by poor, fast-growing countries that increasingly rely on coal to satisfy their growing energy demand. The low price of coal relative to gas and oil has played an important role in accelerating coal consumption since the end of the 1990s. In this article, we show that in the increasingly integrated global coal market the availability of a domestic coal resource does not have a statistically significant impact on the use of coal and related emissions. These findings have important implications for climate change mitigation: If future economic growth of poor countries is fueled mainly by coal, ambitious mitigation targets very likely will become infeasible. Building new coal power plant capacities will lead to lock-in effects for the next few decades. If that lock-in is to be avoided, international climate policy must find ways to offer viable alternatives to coal for developing countries.

BatSLAM: Simultaneous localization and mapping using biomimetic sonar.

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.

A novel biomimetic sonarhead using beamforming technology to mimic bat echolocation.

A novel biomimetic sonarhead has been developed to allow researchers of bat echolocation behavior and biomimetic sonar to perform experiments with a system similar to the bat¿s sensory system. The bat's echolocation-related transfer function (ERTF) is implemented using an array of receivers to implement the head-related transfer function (HRTF), and an array of emitters mounted on a cylindrical manifold to implement the emission pattern of the bat. The complete system is controlled by a field-programmable gate array (FPGA) based embedded system connected through a USB interface.