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Biologically inspired algorithms for simultaneous localization and mapping (SLAM) such as RatSLAM have been shown to yield effective and robust robot navigation in both indoor and outdoor environments. One drawback however is the sensitivity to perceptual aliasing due to the template matching of low-dimensional sensory templates. In this paper, we propose an unsupervised representation learning method that yields low-dimensional latent state descriptors that can be used for RatSLAM. Our method is sensor agnostic and can be applied to any sensor modality, as we illustrate for camera images, radar range-doppler maps and lidar scans. We also show how combining multiple sensors can increase the robustness, by reducing the number of false matches. We evaluate on a dataset captured with a mobile robot navigating in a warehouse-like environment, moving through different aisles with similar appearance, making it hard for the SLAM algorithms to disambiguate locations., Accepted for publication at IEEE ICRA2021

Nowadays, stepper motors are extensively used in positioning applications due to excellent open-loop accuracy and a relatively simple control principle. Every time the controller sends a step-command pulse to the motor, the rotor will move for a known discrete angle. By subsequently counting the number of pulses, the rotor angle is known at all times. Nevertheless, due to the control principle's nature, as a matter of safety, the bulk of stepper motors are often not driven at their full potential to prevent so-called step-losses. Typically, this results in low energy efficiency and an over-dimensioned motor. As a solution, maximizing the motor's load potential through intelligent algorithms contributes to smaller motors and increases efficiency since higher motion speeds are reachable. Until now, in search of optimal motor usage for point-to-point motion profiles, literature mainly focused on finding time-optimal motion profiles using simplified models with a complicated analytical approach rather than developing an easily executable methodology that optimizes at the fundamental control level. Therefore, this paper presents a novel optimization methodology, solely based on the motor's load angle, of which the resulting puls commands' timings can be easily deployed in commercial stepper motor drives. Results show a significant improvement in time-saving of 36,45% compared to a reference 5th-order polynomial point-to-point trajectory.

In this paper, we present an experimental analysis of the Angle of Arrival (AoA) estimation accuracy in an indoor environment. We utilized an AoA estimation system that is suitable for Low Power Wide Area Network (LPWAN) technologies. The AoA estimation system constituted 8 antenna elements that are distributed as Uniform Linear Array (ULA) antenna. Both Line-of-Sight (LoS) and Non-Line-of-Sight (NLoS) conditions were considered. The conventional beamformer, MUISC, Root MUSIC, ESPRIT and SAGE algorithms were employed to provide the AoA estimates. The experimental results reveal that the AoA estimation algorithms provide a very poor AoA estimation accuracy for signals that were originated within the endfire region of the ULA. Furthermore, the signals that were originated within the ULA broadside region have a maximum estimation error equals 10 and 15 degrees for the LoS and NLoS conditions, respectively.

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.

In this paper, we present a new Angle of Arrival (AoA) estimation system for Internet of Things (IoT) applications. The proposed system utilizes the received signal strength (RSS) to estimate the AoA of the received signal in the 868 MHz frequency band. The system consists of two sub-systems: Sub-system 1 utilizes a Rotman lens to provide analog beamforming capabilities and Sub-system 2 utilizes the sparsity-based regularized minimization technique to estimate the AoA of the received signal based on the RSS values. An experiment has been conducted in an anechoic chamber to validate the AoA estimation accuracy. The experimental results reveal that the proposed system can accurately estimate the AoA of the received signals based on the RSS values.

State-of-the-art autonomous vehicles mainly rely on optical sensors to perceive their environment. However, the performance of these sensors worsens dramatically in environments where airborne particles are present. Sonar sensors rely on acoustic waves which are able to pass through these distortions. The data gathered from sonar could complement the distorted data of the optical sensors in these earlier mentioned environments. In this paper we will discuss the newest 3D in-air sonar sensor developed by CoSys-Lab: the micro Real Time Imaging Sonar (mu RTIS). It is the smaller version of the previously developed embedded Real Time Imaging Sonar (eRTIS) and consists of a uniform rectangular array of 5 by 6 microphones compared to the pseudo-random 32 microphone array based on Poisson Disc-Sampling used with the eRTIS. In this paper we will discuss the used hardware of the mu RTIS, followed by the implemented processing algorithms to form acoustic images from reflected ultrasonic data. Furthermore, we will discuss the spatial resolution that can be obtained with the presented hardware architecture using different beamforming algorithms. Finally, we will validate the real-life performance, comparing the acoustic images of the mu RTIS when using Delay-And-Sum or MUltiple SIgnal Classification to solve the different directions of arrival.

This article presents sensor fusion techniques for ultra-wideband-based localization to achieve sufficient accuracy and robustness for the control of vehicles in an industrial environment. We propose two outlier detection methods in combination with an extended Kalman Filter, and present experimental validation where 10 cm accuracy is achieved even in difficult NLOS conditions.

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.

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.

In this paper we present a flexible low-cost sonar sensor platform that can be used for a wide range of biomimetic sonar experiments and autonomous sonar navigation targeted at robotics applications. The navigation abilities of bats using ultrasound (sonar) in unknown cluttered environments are very effective and can be distilled into a sensor architecture and accompanying control methodology that lends itself to be implemented on cost efficient hardware. The sensor architecture and processing methodology of this sensing platform mimics that of bats. In this paper we specifically focused on the common big-eared bat (Micronycteris microtis) although this could be transferred to other bat species or even other echolocating animals since the experimental platform was designed for flexibility. Using this platform we were able to implement a control system using a subsumption architecture that features different behavior patterns based solely on the sonar sensor as a source of exteroceptive information. In order to validate the combination of our autonomous navigation control system and our developed sonar sensor platform, the hardware was mounted on the P3DX robotics platform that was introduced in an unknown testing environment and have it drive autonomously. These experiments were used to validate our assumption of the efficacy of these relatively simple biomimetic control mechanisms and thus alleviating the need for expensive sensing platforms for certain robotics applications.

Modern three-dimensional (3-D) imaging sonar sensors are perfectly capable of creating awareness of the complete nearby environment with great accuracy. However, as range increases, these systems start to struggle with object detection as the poor attenuation of sound through air causes the signal-to-noise ratio to become too small. When looking at other sensing modalities, such as radar, this problem is tackled by using systems that utilize more than one element in the sending and/or receiving stage resulting in a greater effective detection range. At this further distance, the resolution of the obtained image will tend to decrease. Both radar and underwater sonar systems make use of synthetic aperture systems to increase the spatial imaging resolution. In this letter, we combine both techniques to create an in-air (simultaneous transmit) synthetic aperture beamforming (STSTAB) sensor, based on the eRTIS 3-D sonar sensor, that will increase the system's range and angular resolution without sacrificing the measurement rate. Different aspects of the emitter array will be addressed, such as the optimal placement of the elements of the emitter array, along with the coded signals that will be emitted simultaneously. The result will be a fast-measuring imaging sonar sensor with a narrow point-spread-function and improved range.

In this paper, we present a low cost Angle of Arrival (AoA) estimation unit that can be used as an Internet of Things (IoT) receiver and provides AoA estimations of the received signals. This AoA unit built based on converting multiple individual low cost Software Defined Radio (SDR) into a single SDR with multiple RF-channels. The proposed hardware has been tested in an anechoic chamber with an open source IoT standard. The experimental results show that the proposed AoA unit provides a very stable and accurate AoA estimations.

This paper presents a novel biomimetic radar sensor for autonomous navigation. To accomplish this, we have drawn inspiration from the sensory mechanisms present in an echolocating mammal, the common big-eared bat (Micronycteris microtis). We demonstrate the correspondence in both the hardware, system model, and signal processing. To validate the performance of the sensor, we have developed a complementary control system based on subsumption architecture, which allows the system to autonomously navigate unknown environments. This architecture consist of separate behaviors with different levels of complexity, which are combined to produce the overall functionality of the system. We describe each behavior separately and examine their performance in real-world navigation experiments. For this purpose, the system is placed in two distinct office environments with the goal of achieving smooth and stable trajectories. Here, we can observe noticeable improvements when employing high-level behaviors. Furthermore, we utilize the data collected during the navigation experiments to perform simultaneous localization and mapping, using an algorithm developed in our earlier work. These results show a substantial improvement over the odometry. We attribute this to the fact that the system traverses stable and repetitive paths, which facilitates place recognition.

Many popular advanced sonar systems provide accurate and reliable measurements containing crucial info needed by robotic applications such as range, bearing and reflection strength of the objects in the field of view. While these sensor systems provide these crucial pieces of information accurately, they are often limited by a lack of processing power and/or size which leads to them needing an external computing device to process all the information generated by the microphone array on the sensor. In this paper we present two versions of a novel fully embedded 3D sonar sensor which have different sensing architectures which enable 3D perception for robotic application in harsh conditions using ultrasound at low cost. Experimental results taken from an office environment will show the 3D localization capabilities and performance of the sensor, showing the sensor has a large field-of-view (FoV) with accurate 3D localization combined with real-time capabilities.

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.

Economic Growth, Human Development, and Welfare" of the 2018 Report of the International Panel on Social Progress (IPSP). Mission of the IPSP: The International Panel on Social Progress (IPSP) will harness the competence of hundreds of experts about social issues and will deliver a report addressed to all social actors, movements, organizations, politicians and decision-makers, in order to provide them with the best expertise on questions that bear on social change. The Panel will seek consensus whenever possible but will not hide controversies and will honestly present up-to-date arguments and analyses, and debates about them, in an accessible way. The Panel will have no partisan political agenda, but will aim at restoring hope in social progress and stimulating intellectual and public debates. Different political and philosophical views may conceive of social progress in different ways, emphasizing values such as freedom, dignity, or equality. The Panel will retain full independence from political parties, governments, and organizations with a partisan agenda. While the Panel will primarily work for the dissemination of knowledge to all relevant actors in society, it will also foster research on the topics it will study and help to revive interest for research in social long-term prospective analysis

In this paper, we describe the application of a emitter array based on Simultaneous Transmit - Synthetic Aperture Beamforming (ST-STAB) to improve the Signal-to- Noise Ratio (SNR) in an existing, 3D, air-coupled sonar system. The ST-STAB approach allows for the simultaneous emission of orthogonal codes by an array of emitters, which are decoded in the receiver stage, eliminating the need for multiple measurement cycles. By adding more emitters to the sensor system, the overall emitted acoustic energy is increased, while the ST-STAB approach ensures an efficient use of measurement time. We elaborate on the mathematical model of the sensor and signal processing system, provide the associated Point Spread Functions and show experimental validation the constructed prototype array.

In this paper we present a new technique to synchronize multiple individual unsynchronized subarray antenna systems to form one large aperture array antenna system. The proposed technique synchronizes the time and the frequency of the different sub array antenna systems, using a joint Maximum Likelihood (ML) optimization for a data aided received signal, while the phase coherency between the sub arrays is achieved using a combination of least mean square (LMS) and ML algorithms. The experimental results show that the proposed technique is stable in estimating the global array antenna pattern and converges within the first 50 samples of the received data.

Source localization using compressed sensing in the context of indoor RF-based AoA localization has been theoretically studied in the past. These theoretical analysis has revealed that using compressive sensing techniques for small RF antenna arrays is feasible. In this paper we present a hardware architecture which implements the random projections needed for compressive sensing, implemented using 2-port and 4-port lumped Wilkinson power dividers. We introduce the hardware architecture and how the topology of the system can be altered to achieve the necessary decorrelation in the projection process. We illustrate the efficacy of our proposed approach through a source localization experiment.

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 in how to construct such a device using off-the-shelf components.

In many angle of arrival (AoA) estimation algorithms for broadband signals a-prior knowledge about the impinging signals' bandwidth is required for the algorithms to function. In this paper, we present a new technique for estimating the AoA and the bandwidth of the received broadband signals without requiring any knowledge of the bandwidth of the received signals. The proposed technique consists of a uniform circular array (UCA) followed by a transversal filter. It employs variable bandwidth spatial vectors along with the signal to thermal noise ratio (STNR) estimator to estimate the AoA and the bandwidth of the received signals simultaneously. The simulation results illustrate the capabilities of the proposed technique in estimating not only the elevation and the azimuth of the impinging signals with different bandwidths, but also the bandwidths of these received signals.

Motion capture and human body pose estimation systems have become a more common appliance nowadays because of the movie and video game industry. These measurement systems have been proven to be useful for other applications besides entertainment. One of these applications is motion analysis, which can be used for improving the form of athletes or for providing an objective validation tool for rehabilitation treatments. These analyses are done using high-accuracy measurement systems which result in high costs. Although there are some consumer products (e.g. the Microsoft Kinect) that offer movement tracking at a low cost, the accuracy does not suffice for clinical movement analysis applications. This paper therefore focuses on reducing the cost of a human body pose estimation system while retaining the required accuracy. The proposed solution comprises of an embedded ultrasonic transmitter and receiver subsystem. The receiver subsystem consists of multiple mobile nodes that are equipped with a small microphone array (at least three microphones). Each mobile receiver node captures the encoded simultaneously broadcast ultrasonic transmissions from a distributed transmitter array (which consists of at least three elements). Using signal processing, a distance can be calculated between each transmitter and microphone resulting in at least nine distances for each mobile node. Using these distances in combination with the position of the transmitters and the microphone array configuration, the XYZ-position of the mobile node and its rotation about these axes (six degrees-of-freedom) can be estimated. The combination of low-cost embedded and ultrasonics hardware that forms the transducer and receiver subsystem (consisting of multiple mobile receiver nodes) together with powerful signal processing techniques yields a high-accuracy pose estimation system, which can be used as an affordable tool in various fields and applications (e.g., gait analysis for rehabilitation purposes).

Designing model-based physical systems has growing demand in consequence of increasing system complexity. In particular, observers/estimators are extensively used for the applications requiring state or disturbance estimation. Designing and deploying such numerically intensive physical systems onto embedded targets is a challenging task that requires co-design among various stakeholders from different technical backgrounds. The most important challenge is to obtain a numeric behavior of the estimator from an embedded target, that is able to represent the physical system states/disturbance with an acceptable error margin. Moreover, this error margin needs to be decided by the stakeholders, which makes the overall embedded deployment a co-design problem. The main contribution of this paper is to investigate the cause of the estimation error of an estimator that is deployed to embedded targets. This error is studied in the form of precision loss in addition to the error originating in the decreasing estimator measurement frequency for the embedded targets. We propose Assume-Guarantee (A/G) contracts to reconcile the viewpoints of the stakeholders, who reside at different abstraction levels. The feasibility of the proposed physical system deployment method is presented by utilizing a model-based virtual sensor estimator deployment for embedded targets as a case study.

Moving a sensor through its environment creates signature time variations of the sensor’s readings often referred to as flow cues. We analyze the acoustic flow field generated by a sonar sensor, capable of imaging the full frontal hemisphere, mounted on a mobile platform. We show how the cues derived from this acoustic flow field can be used directly in a layered control strategy, which supports a robotic platform to perform a set of motion primitives, such as obstacle avoidance, corridor following, and negotiating corners and T-junctions. The programmable nature of the spatial sampling pattern of the sonar allows efficient support of the varying information requirements of the different motion primitives. The proposed control strategy is first validated in a simulated environment and subsequently transferred to a real mobile robot. We present simulated and experimental results on the controller’s performance while executing the different motion primitives. The results further show that the proposed control strategy can easily integrate minimal steering commands given by a user (electric wheelchair application) or by a high-level navigation module (autonomous simultaneous localization and mapping (SLAM) applications).

In this paper we present a multi-scale microphone array aimed at 3D source localization, with applications in bioacoustic animal tracking. We propose a hardware architecture for wirelessly synchronized sensor nodes consisting of small 5-element microphone arrays. Using this distributed sensor array we set up a probabilistic source localization problem, which we subsequently demonstrate to be able to estimate the position of a bat-like sound source in three dimensions. The proposed hardware architecture allows scalable source localization in a wide variety of application domains.

This paper introduces and validates the performance of an alternative input device for people with limited hand/arm movement and control. A summary is provided of the current state of the art in alternative input devices. Based on this, a low-cost solution is proposed that allows for 1) low latency and high operating speed and accuracy, 2) use from different viewing angles without recalibration, and 3) the ability to seamlessly control multiple devices. A prototype of this system was built and tested to determine the accuracy of the system (using a pan–tilt system), and to analyze the performance of the system compared to the state of the art (through user tests, based on the ISO 9241–411 test). The proposed system allows for great accuracy ( $\sigma _X=\text{0.28 px}, \sigma _Y=\text{0.29 px}, \sigma _{XY}=\text{0.02 px}$ ), a decent performance compared to the state of the art (throughput = 1.52 bits/s, error rate = 31%), and good results in the ISO 9241-411 independent rating scale. The results from the performed experiments show that the proposed system leads great promise in real-world applications. A low-cost head-mounted camera could be used as an alternative human interface device for people with limited hand/arm movement and control, allowing them to participate in the ongoing trend of computing devices gaining importance in our everyday activities.

Modern localization and/or mapping systems based on ultrasound use multiple types of ultrasound transducers. These transducers are used to scan the environment and create situational awareness so that obstacles are spotted and collisions may be prevented. Most commercially available transducers however have a field of view (FOV) that is too limited for these applications. As a result of this, multiple transducers are necessary to get a decent perceptional coverage of the environment. The signals coming from the different transducers can cause unnecessary complications. In this paper, we propose a method for widening the FOV of off-the-shelf ultrasonic transducers by creating a baffle that fits on these transducers. This will significantly improve the process of mapping the environment for a wide range of applications, such as obstacle avoidance and SLAM. We provide strong experimental evidence for the widening of the directivity patterns by means of various 3-D-printed baffles.

This paper presents a novel method for using an ultra-wideband (UWB), super high frequency (SHF) pulse-echo radar sensor as a biomimetic sensing mechanism to successfully solve the Simultaneous Localization and Mapping (SLAM) problem. Due to recent advances in consumer radar technology it has become possible to sample the received echo signals well above their Nyquist frequency. This Nyquist-conform sampling permits the conversion of the signal waveforms into spectrograms which contain spatiospectral cues caused by the interactions of the echoes with features of the environment and the antenna's radiation pattern. Such spectrograms can therefore serve as distinct labels for individual locations, allowing for the identification and recognition of these locations. By adapting an existing acoustic SLAM system (BatSLAM) we have developed a system that demonstrates the feasibility of our proposed method; the results validate the potential of using pulse-echo radar as an exteroceptive sensory modality in topological SLAM systems.

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.

Passive acoustic localization is an important technique in a wide variety of monitoring applications, ranging from healthcare over biological survey to structural health monitoring of buildings. If the subject of interest emits a recognizable sound and is picked up by a distributed array of microphones it is possible to measure the difference in arrival times and reconstruct the source positions. This requires the microphones to be synchronized up to a fraction of the expected time differences of arrivals in order for the system to be able to produce accurate location estimates. In this paper we take a closer look at the techniques required to synchronize a network of sensors, connected through a low-power RF-communication link, in a distributed manner, ie. without the presence of a master node. The absence of a master node makes the network more robust against the failure of a single node. We took inspiration from the synchronization technique observed in some species of fireflies. A synchronized pseudo random number is embedded in the captured data as a marker to re-align the data-streams in time in a post-processing stage.

Obstructive lung disease is a category of respiratory disease characterized by an obstruction of the airflow, inflamed and/or easily collapsible airways, and mucus retention. Asthma, bronchiectasis, and chronic obstructive pulmonary disease (COPD) are the three main types of obstructive lung diseases. This type of diseases causes adventitious lung sounds which can be heard through lung auscultation. Physiotherapists can detect these sounds and use them to adapt their treatment. Physicians the world over are starting to rely more on computers than ever before. For lung auscultation data, this is mostly limited to representing the sound as a time-pressure graph or a spectrogram, while still doing the actual analysis themselves. Researchers however, have already shown the strength of computational lung sound analysis. In this paper, we propose the Joint Acoustic-and Modulation Frequency (JAMF) representation as a signal-processing technique for a lung-sound sensor which create a visually clean, simple and yet powerful representation which allows physicians to determine possible problems at first glance, with future possibilities for easy automatic analysis of the lung sounds.

In this paper, we present a head-mounted pointing device (i.e. "Head-mouse") which consists of an optical sensor based on a small CMOS-camera with the IR blocking filter replaced by an IR-bandpass filter and IR LEDs (850nm wavelength) as markers. The advantage of our sensor compared to other head-mouse systems and eye-gaze systems is that the proposed sensor results in an absolute positioning of the mouse cursor and does not require laborious recalibration as the user changes his position. Furthermore, the sensor has a larger operating area and allows seamless control of multiple devices.

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.

Motion capture systems have become a common utility in a wide variety of fields and rely on various underlying principles to estimate the pose of one or multiple mobile objects. The most popular applications for pose estimation systems can be found in the entertainment sector where it is used for computer animation purposes or in sports for performance measurements. These systems present millimeter accuracy but also come with a high cost. We wish to reduce the cost of such a motion capture system while retaining the high accuracy by using an embedded ultrasonic measurement system. The proposed system comprises of one or multiple mobile nodes containing a small microphone array (at least 3 microphones) which receives multiple simultaneously emitted coded and broadband ultrasonic transmissions from at least three fixed transmitters. Using this technique, the complete pose (six degrees-of-freedom) of every mobile sensor can be estimated while retaining the low-cost aspect of the system. Motion capture systems which combine low cost with high accuracy open many new fields of application such as physiotherapy practices.

Recently we have developed an in-air sonar system that is capable of generating 3D descriptions of environments containing multiple reflectors using a single broadband emission in combination with a microphone array. We have coined the term "energyscape" for the representation of the environment, i.e. reflected energy as a function of range and direction, generated by this system. While such rich sensor data can prove useful without object segmentation for certain applications, it is sometimes desirable to map the energyscapes onto individual reflector positions. Simple ad-hoc segmentation methods often have problems with the high dynamic range in the sensor data. We propose a method using sparse decomposition of the energyscapes using a dictionary containing direction dependent Point Spread Functions (PSF) of the sensor system. Using an L1-regularized least squares solution method we demonstrate reconstruction of actual measured energyscapes and estimation of reflector positions in complex scenarios.

Array beamforming techniques allow for the generation of 3-D spatial filters which can be used to localize objects in a large field of view (FOV) without the need for mechanical scanning. By combining broadband beamforming with a sparse, random array of receivers, we have constructed a low-cost, yet powerful, in-air sonar system, which is suited for a wide range of robotic applications. Experimental results in unmodified office environments show the performance of the sonar sensor. In particular, we document the sensor's capacity to produce 3-D location measurements in the presence of multiple highly overlapping echoes. We show how this capability makes possible the combination of a wide FOV with accurate 3-D localization, allowing the sensor to operate under real-time constraints in realistic environments. To demonstrate the use of this sensor, we describe an odometry application that estimates egomotion of a mobile robot using acoustic flow.

We present a biomimetic sonar system consisting of a single emitter and two receivers endowed with realistic spatial filter characteristics by inserting them in plastic replicas of bat pinnae. The outputs of these receivers are processed by a simple functional model of the processing performed in the cochlea. We show that these cochleograms contain sufficient amounts of information for the robust localization of a robot in unmodified office environments. This localization component is part of a more general biomimetic Simultaneous Localization and Mapping system (SLAM) for an autonomous robot based on a model of the rodent Hippocampus and the use of sonar instead of vision as exteroceptive sensory input (BatSLAM). To quantify the performance of the biomimetic sonar system in the robot localization task, and to be able to quantitatively compare the performance of different types of (sonar) sensors, we propose a technique based on conditional entropy.

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Biomimetic sonar systems, i.e. sonar systems making use of spectral cues for the localization of one or more reflectors, depend heavily on the spatial filters of the reception subsystem. These spatial filters can be implemented in two ways, e. g., by means of an artificial pinna or by means of an array of microphones in combination with a beamforming algorithm. In this paper we compare two such systems using an information theoretic model, which allows objective evaluation of each system from an echolocation point of view.

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 1 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 1 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.

In this study we investigate the effects of medium disturbances on the usability of a biomimetic sonar system in a target localization application. In particular, we concentrate on a fire-fighting application giving rise to specific types of medium disturbances. We determine experimentally the effects of these medium disturbances: undisturbed air (reference), the presence of small water droplets, large convective air-currents and flames, on the received echo spectra. As the echo spectra are used to estimate the target location, these different conditions by disturbing to varying extents sound propagation through the medium will have an effect on the sonar system's localization performance. Using an information theoretic model of the spectrum based localization mechanism we characterize the loss in localization accuracy due to the different medium disturbances. We conclude that an echolocation system based on the sonar system of the bat Phyllostomus discolor allows for robust localization even in highly disturbed media. However, while the need for replacing the emission pattern of the bat by that of a standard transducer in the biomimetic sonar system makes little difference in the ‘reference’ and ‘water spray’ conditions it limits echolocation performance in the ‘air-currents’ condition and makes the use of the proposed system very difficult in the ‘flames’ condition.

This paper presents results from a model intercomparison exercise among regionalized global energy-economy models conducted in the context of the RECIPE project. The economic adjustment effects of long-term climate policy aiming at stabilization of atmospheric CO2 concentrations at 450 ppm are investigated based on the cross-comparison of the intertemporal optimization models REMIND-R and WITCH as well as the recursive dynamic computable general equilibrium model IMACLIM-R. The models applied in the project differ in several respects and the comparison exercise tracks differences in the business as usual forecasts as well as in the mitigation scenarios to conceptual differences in the model structures and assumptions. In particular, the models have different representation of the sectoral structure of the energy system. A detailed sectoral analysis conducted as part of this study reveals that the sectoral representation is a crucial determinant of the mitigation strategy and costs. While all models project that the electricity sector can be decarbonized readily, emissions abatement in the non-electric sectors, particularly transport, is much more challenging. Mitigation costs and carbon prices were found to depend strongly on the availability of low-carbon options in the non-electric sectors.

Bats are known to use spectral cues to localise targets. We propose a similar localisation scheme making use of a sensor array built using EMFi material and a structured backplate. We present an adaptation of `delay and sum? beamforming that when combined with a broadband chirp results in accurate localisation (±2°) in a broad field of view [?70°,70°].

Bats are known to use spectral cues to localise. targets. We propose a similar localisation scheme making use of a sensor array built using EMFi material and a structured backplate. We present an adaptation of 'delay and sum' beamforming that when combined with a broadband chirp results in accurate localisation (+/- 2 degrees) in a broad field of view [-70 degrees,70 degrees].