Acoustic Sensor Networks and Mobile Robotics for Sound Source Localization

© 2019 IEEE. Localizing a sound source is a fundamental but still challenging issue in many applications, where sound information is gathered by static and local microphone sensors. Therefore, this work proposes a new system by exploiting advances in sensor networks and robotics to more accurately address the problem of sound source localization. By the use of the network infrastructure, acoustic sensors are more efficient to spatially monitor acoustical phenomena. Furthermore, a mobile robot is proposed to carry an extra microphone array in order to collect more acoustic signals when it travels around the environment. Driving the robot is guided by the need to increase the quality of the data gathered by the static acoustic sensors, which leads to better probabilistic fusion of all the information gained, so that an increasingly accurate map of the sound source can be built. The proposed system has been validated in a real-life environment, where the obtained results are highly promising

Multilevel B-Splines-Based Learning Approach for Sound Source Localization

© 2001-2012 IEEE. In this paper, a new learning approach for sound source localization is presented using ad hoc either synchronous or asynchronous distributed microphone networks based on the time differences of arrival (TDOA) estimation. It is first to propose a new concept in which the coordinates of a sound source location are defined as the functions of TDOAs, computing for each pair of microphone signals in the network. Then, given a set of pre-recorded sound measurements and their corresponding source locations, the multilevel B-splines-based learning model is proposed to be trained by the input of the known TDOAs and the output of the known coordinates of the sound source locations. For a new acoustic source, if its sound signals are recorded, the correspondingly computed TDOAs can be fed into the learned model to predict the location of the new source. Superiorities of the proposed method are to incorporate the acoustic characteristics of a targeted environment and even remaining uncertainty of TDOA estimations into the learning model before conducting its prediction and to be applicable for both synchronous or asynchronous distributed microphone sensor networks. The effectiveness of the proposed algorithm in terms of localization accuracy and computational cost in comparisons with the state-of-the-art methods was extensively validated on both synthetic simulation experiments as well as in three real-life environments

Can a Robot Hear the Shape and Dimensions of a Room?

© 2019 IEEE. Knowing the geometry of a space is desirable for many applications, e.g. sound source localization, sound field reproduction or auralization. In circumstances where only acoustic signals can be obtained, estimating the geometry of a room is a challenging proposition. Existing methods have been proposed to reconstruct a room from the room impulse responses (RIRs). However, the sound source and microphones must be deployed in a feasible region of the room for it to work, which is impractical when the room is unknown. This work propose to employ a robot equipped with a sound source and four acoustic sensors, to follow a proposed path planning strategy to moves around the room to collect first image sources for room geometry estimation. The strategy can effectively drives the robot from a random initial location through the room so that the room geometry is guaranteed to be revealed. Effectiveness of the proposed approach is extensively validated in a synthetic environment, where the results obtained are highly promising

Adaptive sampling for spatial prediction in environmental monitoring using wireless sensor networks: A review

© 2018 IEEE. The paper presents a review of the spatial prediction problem in the environmental monitoring applications by utilizing stationary and mobile robotic wireless sensor networks. First, the problem of selecting the best subset of stationary wireless sensors monitoring environmental phenomena in terms of sensing quality is surveyed. Then, predictive inference approaches and sampling algorithms for mobile sensing agents to optimally observe spatially physical processes in the existing works are analysed

Improved signal interpretation for cast iron thickness assessment based on pulsed eddy current sensing

© 2017 IEEE. This paper presents a novel signal processing approach for computing thickness of ferromagnetic cast iron material, widely employed in older infrastructure such as water mains or bridges. Measurements are gathered from a Pulsed Eddy Current (PEC) based sensor placed on top of the material, with unknown lift-off, as commonly used during non-destructive testing (NDT). The approach takes advantage of an analytical logarithmic model proposed in the literature for the decaying voltage induced at the PEC sensor pick-up coil. An increasingly more accurate and robust algorithm is proven here by means of an Adaptive Least Square Fitting Line (ALSFL) recursive strategy, suitable to recognize the most linear part of the sensor's logarithmic output voltage for subsequent gradient computation, from which thickness is then derived. Moreover, efficiency is also gained as processing can be carried out on only one decaying voltage signal, unlike averaging over multiple measurements as is usually done in the literature. Importantly, the new signal processing methodology demonstrates highest accuracies at the lower thicknesses, a circumstance most relevant to NDT evaluation. Experiments that verify the proposed method in real-world thickness assessment of cast iron material are presented and compared with current practices, showing promising results

Design of a lock-in amplifier integrated with a coil system for eddy-current non-destructive inspection

© 2017 IEEE. Eddy-current non-destructive inspections of conductive components are of great interest in several industries including civil infrastructure and the mining industry. In this work, we have used a driver-pickup coil system as the probe to carry out inspection of ferromagnetic plates. The specific geometric configuration of the probe generates weak electric signals that are buried in a noisy environment. In order to detect these weak signals, we have designed and implemented a lock-in amplifier as part of the signal processing technique to increase the signal-to-noise ratio and also improve the sensitivity of the probe. We have used Comsol as a finite element method (FEM) to design the probe and conducted experiments with the probe and the lock-in amplifier. The experimental results, which are in agreement with the FEM results, indicate that the designed probe along with a lock-in amplifier can potentially be used to estimate the thickness of thin plates

Designing a pulsed eddy current sensing set-up for cast iron thickness assessment

© 2017 IEEE. Pulsed Eddy Current (PEC) sensors possess proven functionality in measuring ferromagnetic material thickness. However, most commercial PEC service providers as well as researchers have investigated and claim functionality of sensors on homogeneous structural steels (steel grade Q235 for example). In this paper, we present design steps for a PEC sensing set-up to measure thickness of cast iron, which is unlike steel, is a highly inhomogeneous and non-linear ferromagnetic material. The setup includes a PEC sensor, sensor excitation and reception circuits, and a unique signal processing method. The signal processing method yields a signal feature which behaves as a function of thickness. The signal feature has a desirable characteristic of being lowly influenced by lift-off. Experimental results show that the set-up is usable for Non-destructive Evaluation (NDE) applications such as cast iron water pipe assessment

The core Planar Cell Polarity gene, Vangl2, maintains apical-basal organisation of the corneal epithelium

This work was performed under Biotechnology and Biological Sciences Research Council (BBSRC) research grant BB/J015237/1 to JMC. DAP was funded by an Anatomical Society PhD Studentship whose support is gratefully acknowledged. ASF was funded by a BBSRC DTG PhD Studentship. We thank staff at the Medical Research Facility and Aberdeen Microscopy Services for technical assistance.Peer reviewedPostprin

Optimisation of Interface Roughness and Coating Thickness to Maximise Coating-Substrate Adhesion - A Failure Prediction and Reliability Assessment Modelling

This paper addresses a novel modelling technique which is based on a multidisciplinary approach to predict the coating-substrate adhesion. It proposes new equations governing coating debondment that combines material science concepts with and solid mechanics concepts. The effects of two parameters i.e. interface roughness λ and coating thickness h on coating-substrate adhesion has been analysed. The reliability of newly developed technique has been validated by comparison with the experimental results

Hypothalamic arcuate nucleus glucokinase regulates insulin secretion and glucose homeostasis

Aims Glucokinase (GK) serves as a glucose sensor in several tissues including glucose‐sensitive neurons of the arcuate nucleus within the hypothalamus. We have previously demonstrated a role for arcuate GK in the regulation of food and glucose intake. However, its role in the regulation of glucose homeostasis is less clear. We therefore sought to investigate the role of arcuate GK in the regulation of glucose homeostasis. Materials and Methods Recombinant adeno‐associated virus expressing either GK or an antisense GK construct was used to alter GK activity specifically in the hypothalamic arcuate nucleus. GK activity in this nucleus was also increased by stereotactic injection of the GK activator, compound A. The effect of altered arcuate nucleus GK activity on glucose homeostasis was subsequently investigated using glucose and insulin tolerance tests. Results Increased GK activity specifically within the arcuate nucleus increased insulin secretion and improved glucose tolerance in rats during oral glucose tolerance tests. Decreased GK activity in this nucleus reduced insulin secretion and increased glucose levels during the same tests. Insulin sensitivity was not affected in either case. The effect of arcuate nucleus glucokinase was maintained in a model of type 2 diabetes. Conclusions These results demonstrate a role for arcuate nucleus GK in systemic glucose homeostasis